Dynamic calibration systems and methods for wearable heads-up displays

ABSTRACT

Systems, methods and articles that provide dynamic calibration of eye tracking systems for wearable heads-up displays (WHUDs). The eye tracking system may determine a user&#39;s gaze location on a display of the WHUD utilizing a calibration point model that includes a plurality of calibration points. During regular use of the WHUD by the user, the calibration point model may be dynamically updated based on the user&#39;s interaction with user interface (UI) elements presented on the display. The UI elements may be specifically designed (e.g., shaped, positioned, displaced) to provide in-use and on-going dynamic calibration of the eye tracking system, which in at least some implementations may be unnoticeable to the user.

BACKGROUND Technical Field

The present disclosure generally relates to wearable heads-up displays,and more particularly, to calibration systems and methods for eyetracking systems of wearable heads-up displays.

Description of the Related Art

Wearable Heads-Up Displays

A head-mounted display is an electronic device that is worn on a user'shead and, when so worn, secures at least one electronic display within aviewable field of at least one of the user's eyes, regardless of theposition or orientation of the user's head. A wearable heads-up displayis a head-mounted display that enables the user to see displayed contentbut also does not prevent the user from being able to see their externalenvironment. The “display” component of a wearable heads-up display iseither transparent or at a periphery of the user's field of view so thatit does not completely block the user from being able to see theirexternal environment. Examples of wearable heads-up displays include:the Google Glass®, the Optinvent Ora®, the Epson Moverio®, and the SonyGlasstron®, just to name a few.

The optical performance of a wearable heads-up display is an importantfactor in its design. When it comes to face-worn devices, however, usersalso care a lot about aesthetics. This is clearly highlighted by theimmensity of the eyeglass (including sunglass) frame industry.Independent of their performance limitations, many of the aforementionedexamples of wearable heads-up displays have struggled to find tractionin consumer markets because, at least in part, they lack fashion appeal.Most wearable heads-up displays presented to date employ large displaycomponents and, as a result, most wearable heads-up displays presentedto date are considerably bulkier and less stylish than conventionaleyeglass frames.

A challenge in the design of wearable heads-up displays is to minimizethe bulk of the face-worn apparatus while still providing displayedcontent with sufficient visual quality. There is a need in the art forwearable heads-up displays of more aesthetically-appealing design thatare capable of providing high-quality images to the user withoutlimiting the user's ability to see their external environment.

Eye Tracking

Eye tracking is a process by which the position, orientation, and/ormotion of the eye may be measured, detected, sensed, determined(collectively, “measured”), and/or monitored. In many applications, thisis done with a view towards determining the gaze direction of a user.The position, orientation, and/or motion of the eye may be measured in avariety of different ways, the least invasive of which typically employone or more optical sensor(s) (e.g., cameras) to optically track theeye. Common techniques involve illuminating or flooding the entire eye,all at once, with infrared light and measuring reflections with at leastone optical sensor that is tuned to be sensitive to the infrared light.Information about how the infrared light is reflected from the eye isanalyzed to determine the position(s), orientation(s), and/or motion(s)of one or more eye feature(s) such as the cornea, pupil, iris, and/orretinal blood vessels.

Eye tracking functionality is highly advantageous in applications ofwearable heads-up displays. Some examples of the utility of eye trackingin wearable heads-up displays include: influencing where content isdisplayed in the user's field of view, conserving power by notdisplaying content that is outside of the user's field of view,influencing what content is displayed to the user, determining where theuser is looking or gazing, determining whether the user is looking atdisplayed content on the display or through the display at theirexternal environment, and providing a means through which the user maycontrol/interact with displayed content.

BRIEF SUMMARY

A method of operating a wearable heads-up display device (WHUD)comprising a display and a glint detection module may be summarized asincluding during regular operation of the WHUD by a user, populating, byat least one processor, the display of the WHUD with at least one userinterface (UI) element; and detecting, by the at least one processor, agaze location of a user based at least in part on glint informationreceived from the glint detection module and a known location of atleast one of the at least one UI element on the display. Populating thedisplay of the WHUD with at least one UI element may include populatingthe display with a number of UI elements that is below a thresholddetermined to enable accurate gaze location detection. Populating thedisplay of the WHUD with at least one UI element may include maximizingthe respective distances between a plurality of UI elements displayed onthe display. Populating the display of the WHUD with at least one UIelement may include minimizing a similarity between an angle and alength of vectors that join pairs of a plurality of UI elementsdisplayed on the display. Populating the display of the WHUD with atleast one UI element may include populating the display of the WHUD withat least one animated UI element.

The method may further include updating, by the at least one processor,an eye tracking calibration based at least in part on the detected gazelocation. Updating an eye tracking calibration may include generating atleast one calibration point based at least in part on the detected gazelocation, the at least one calibration point including a glint spacepoint received from the glint detection module and a display space pointthat corresponds to a location of a UI element displayed on the display.Generating at least one calibration point may include generating acalibration point for each UI element displayed on the display, eachcalibration point associated with a UI element including a glint spacepoint received from the glint detection module and a display space pointthat corresponds to the location of the UI element on the display.Updating an eye tracking calibration may include adding a calibrationpoint to at least one parent calibration point model that includes aplurality of calibration points to generate at least one childcalibration point model.

The method may further include fitting, by the at least one processor, atransform to the calibration points of the at least one childcalibration point model.

A wearable heads-up display device (WHUD) may be summarized asincluding: a support frame; a display carried by the support frame; aglint detection module carried by the support frame; at least oneprocessor carried by the support frame, the at least one processorcommunicatively coupled to the display and the glint detection module;and at least one nontransitory processor-readable storage medium carriedby the support frame, the at least one nontransitory processor-readablestorage medium communicatively coupled to the at least one processor,wherein the at least one nontransitory processor-readable storage mediumstores data or processor-executable instructions that, when executed bythe at least one processor, cause the at least one processor to: causethe display to display at least one user interface (UI) element; anddetect a gaze location of a user based at least in part on glintinformation received from the glint detection module and a knownlocation of at least one of the at least one UI element on the display.

The data or processor-executable instructions, when executed by the atleast one processor, may cause the at least one processor to cause thedisplay to display a number of UI elements that is below a thresholddetermined to enable accurate gaze location detection.

The data or processor-executable instructions, when executed by the atleast one processor, may cause the at least one processor to cause thedisplay to maximize the respective distances between a plurality of UIelements displayed on the display.

The data or processor-executable instructions, when executed by the atleast one processor, may cause the at least one processor to cause thedisplay to minimize a similarity between an angle and a length ofvectors that join pairs of a plurality of UI elements displayed on thedisplay.

The data or processor-executable instructions, when executed by the atleast one processor, may cause the at least one processor to cause thedisplay to display at least one animated UI element.

The data or processor-executable instructions, when executed by the atleast one processor, may cause the at least one processor to update aneye tracking calibration based at least in part on the detected gazelocation. The at least one processor may generate at least onecalibration point based at least in part on the detected gaze location,the at least one calibration point comprising a glint space pointreceived from the glint detection module and a display space point thatcorresponds to a location of a UI element displayed on the display. Theat least one processor may generate a calibration point for each UIelement displayed on the display, each calibration point associated witha UI element comprising a glint space point received from the glintdetection module and a display space point that corresponds to thelocation of the UI element on the display. The at least one processormay add a calibration point to at least one parent calibration pointmodel that comprises a plurality of calibration points to generate atleast one child calibration point model. The at least one processor mayfit a transform to the calibration points of the at least one childcalibration point model.

A method of operating a wearable heads-up display device (WHUD)comprising a display and a glint detection module may be summarized asincluding obtaining, by at least one processor, one or more calibrationpoint models each comprising a plurality of calibration points, eachcalibration point comprising: a glint space point in a glint spacecaptured by the glint detection module, the glint space pointrepresentative of a position of an eye of a user of the WHUD; and adisplay space point in a display space of the display, the display spacepoint representative of a location on the display at which a gaze of theuser is inferred to be resting when the glint space point is captured bythe glint detection module; generating, by the at least one processor, atransform from the glint space to the display space for each of the oneor more calibration point models; determining, by the at least oneprocessor, user gaze location in the display space using received glintinformation and the generated transform; from time-to-time duringregular operation of the WHUD by the user, generating, by the at leastone processor, at least one additional calibration point; adding, by theat least one processor, the additional calibration point to at least oneof the calibration point models to generate one or more childcalibration point models; generating, by the at least one processor, atransform for each of the one or more child calibration point models;and determining, by the at least one processor, a user gaze location inthe display space using at least one glint space point received from theglint detection module and at least one transform of the one or morechild calibration point models.

Generating at least one additional calibration point may includegenerating an additional inferred calibration point including a glintspace point received from the glint detection module; and a displayspace point that corresponds to a location in the display space of a UIelement determined to be the user gaze location.

Generating at least one additional calibration point may includegenerating an additional inferred calibration point for each of aplurality of UI elements displayed on the display, each inferredcalibration point including a glint space point received from the glintdetection module; and a display space point that corresponds to alocation in the display space of one of the plurality of UI elements.

Generating at least one additional calibration point may includegenerating at least one additional selected calibration point includinga glint space point received from the glint detection module; and adisplay space point that is a location of a UI element on the displayselected by the user during regular operation of the WHUD. Determining auser gaze location in the display space using at least one glint spacepoint received from the glint detection module and at least onetransform may include determining a user gaze location in the displayspace using at least one glint space point received from the glintdetection module and the one or more child calibration point models thatinclude the at least one additional selected calibration point.Generating a transform may include generating an affine transform fromthe glint space to the display space. Generating a transform may includesolving a matrix utilizing at least one of a QR decomposition method orsingular value decomposition method.

The method may further include from time-to-time during regularoperation of the WHUD by the user, evicting at least one calibrationpoint from a calibration point model. Evicting at least one calibrationpoint from a calibration point model may include evicting an oldestcalibration point from the calibration point model. Evicting at leastone calibration point from a calibration point model may includeevicting a calibration point based on at least one of the locations ofcalibration points in the calibration point model or the times at whichthe calibration points in the calibration point model were obtained.

Obtaining a calibration point model including a plurality of calibrationpoints may include populating, by the at least one processor, thedisplay of the WHUD with a plurality of UI elements; for each of theplurality of UI elements, receiving, by the at least one processor, aselection of the UI element by the user; receiving, by the at least oneprocessor, a glint space point from the glint detection module obtainedconcurrently with the selection of the UI element by the user; andgenerating, by the at least one processor, a calibration point thatcomprises the received glint space point and a display space pointrepresentative of the location of the UI element on the display of theWHUD. Populating the display of the WHUD with a plurality of UI elementsmay include populating the display with the plurality of UI elements oneat a time in a sequential order.

Obtaining a calibration point model including a plurality of calibrationpoints may include causing, by the at least one processor, four UIelements to be sequentially displayed on the display, each of the fourUI elements sequentially displayed in a different one of four corners ofthe display; and obtaining, by the at least one processor, fourcalibration points that each correspond to a respective one of the UIelements, each calibration point comprising a display point the displayspace and a glint space point in the glint space.

Obtaining a calibration point model including a plurality of calibrationpoints may include causing, by at least one processor, a UI element tomove on the display of the WHUD according to a determined pattern; andgenerating, by the at least one processor, a plurality of calibrationpoints as the UI element moves on the display, each calibration pointincludes a glint space point in the glint space captured by the glintdetection module; and a display space point in the display space, thedisplay space point representative of a location on the display of themoving UI element when the corresponding glint space point is capturedby the glint detection module. Causing a UI element to move on thedisplay of the WHUD according to a determined pattern may includecausing a UI element to move on the display of the WHUD according to arectangular-shaped pattern in a first direction and a second direction,the second direction opposite the first direction.

The method may further include receiving, by the at least one processor,at least one auxiliary sensor value from at least one auxiliary sensorduring regular operation of the WHUD by the user; and optimizing, by theat least one processor, a transform of at least one calibration pointmodel based at least in part on the received at least one auxiliarysensor value. Receiving at least one auxiliary sensor value may includeobtaining at least one auxiliary sensor value from at least one of aproximity sensor, a gyroscope sensor or an accelerometer.

The method may further include receiving, by the at least one processor,a plurality of calibration points, each calibration point including aglint space point; a display space point; and at least one auxiliarysensor value from at least one auxiliary sensor obtained concurrentlywith the glint space point and the display space point; and training amachine learning model utilizing the plurality of calibration points, ordata derived therefrom, the trained machine learning model receives asinputs at least one current auxiliary sensor value and outputs at leastone of a set of calibration points or transform parameters.

The method may further include optimizing, by the at least oneprocessor, at least one transform utilizing the trained machine learningmodel. Receiving a plurality of calibration points may include receivinga plurality of calibration points from the WHUD and from a population ofWHUDs operated by a population of users.

A wearable heads-up display (WHUD) may be summarized as including asupport frame; a display carried by the support frame; a glint detectionmodule carried by the support frame that, in operation, determines glintspace points in a glint space that correspond to a region in a field ofview of an eye of a user at which a gaze of the eye is directed; atleast one processor carried by the support frame, the at least oneprocessor communicatively coupled to the display and the glint detectionmodule; and at least one nontransitory processor-readable storage mediumcarried by the support frame, the at least one nontransitoryprocessor-readable storage medium communicatively coupled to the atleast one processor, wherein the at least one nontransitoryprocessor-readable storage medium stores data or processor-executableinstructions that, when executed by the at least one processor, causethe at least one processor to: obtain one or more calibration pointmodels each comprising a plurality of calibration points, eachcalibration point comprising: a glint space point in a glint spacecaptured by the glint detection module, the glint space pointrepresentative of a position of an eye of a user of the WHUD; and adisplay space point in a display space of the display, the display spacepoint representative of a location on the display at which a gaze of theuser is inferred to be resting when the glint space point is captured bythe glint detection module; generate a transform from the glint space tothe display space for each of the calibration point models; determineuser gaze location in the display space using received glint informationand the generated transform; from time-to-time during regular operationof the WHUD by the user, generate at least one additional calibrationpoint; add the additional calibration point to at least one of thecalibration point models to generate one or more child calibration pointmodels; generate a transform for each of the one or more childcalibration point models; and determine a user gaze location in thedisplay space using at least one glint space point received from theglint detection module and at least one transform of the one or morechild calibration point models.

The at least one processor may generate an additional inferredcalibration point including a glint space point received from the glintdetection module; and a display space point that corresponds to alocation in the display space of a UI element determined to be the usergaze location. The at least one processor may generate an additionalinferred calibration point for each of a plurality of UI elementsdisplayed on the display, each inferred calibration point including aglint space point received from the glint detection module; and adisplay space point that corresponds to a location in the display spaceof one of a plurality of UI elements. The at least one processor maygenerate an additional selected calibration point including a glintspace point received from the glint detection module; and a displayspace point that is a location of a UI element on the display selectedby the user during regular operation of the WHUD. The at least oneprocessor may determine a user gaze location in the display space usingat least one glint space point received from the glint detection moduleand the one or more child calibration point models that include theadditional selected calibration point. The at least one processor maygenerate an affine transform from the glint space to the display space.The at least one processor may solve a matrix utilizing at least one ofa QR decomposition method or singular value decomposition method. The atleast one processor may, from time-to-time during regular operation ofthe WHUD by the user, evict at least one calibration point from acalibration point model. The at least one processor may evict an oldestcalibration point from the calibration point model. The at least oneprocessor may evict a calibration point based on at least one of thelocations of calibration points in the calibration point model or thetimes at which the calibration points in the calibration point modelwere obtained.

To obtain a calibration point model including a plurality of calibrationpoints, the at least one processor may populate the display of the WHUDwith a plurality of UI elements; and for each of the plurality of UIelements, receive a selection of the UI element by the user; receive aglint space point from the glint detection module obtained concurrentlywith the selection of the UI element by the user; and generate acalibration point that comprises the received glint space point and adisplay space point representative of the location of the UI element onthe display of the WHUD. The at least one processor may populate thedisplay of the WHUD with a plurality of UI elements one at a time in asequential order. To obtain a calibration point model including aplurality of calibration points, the at least one processor may causefour UI elements to be sequentially displayed on the display, each ofthe four UI elements sequentially displayed in a different one of fourcorners of the display; and obtain four calibration points that eachcorrespond to a respective one of the UI elements, each calibrationpoint comprising a display point the display space and a glint spacepoint in the glint space.

To obtain a calibration point model including a plurality of calibrationpoints, the at least one processor may cause a UI element to move on thedisplay of the WHUD according to a determined pattern; and generate aplurality of calibration points as the UI element moves on the display,each calibration point includes a glint space point in the glint spacecaptured by the glint detection module; and a display space point in thedisplay space, the display space point representative of a location onthe display of the moving UI element when the corresponding glint spacepoint is captured by the glint detection module. The determined patternmay include a rectangular-shaped pattern, and the at least one processormay cause the UI element to move in the rectangular-shaped pattern in afirst direction; and cause the UI element to move in therectangular-shaped pattern in a second direction, the second directionopposite the first direction. The at least one processor may receive atleast one auxiliary sensor value from at least one auxiliary sensorduring regular operation of the WHUD by the user; and optimize atransform of at least one calibration point model based at least in parton the received at least one auxiliary sensor value. The at least oneauxiliary sensor may include at least one of a proximity sensor, agyroscope sensor or an accelerometer.

The at least one processor may receive a plurality of calibrationpoints, each calibration point including a glint space point; a displayspace point; and at least one auxiliary sensor value from at least oneauxiliary sensor obtained concurrently with the glint space point andthe display space point; and train a machine learning model utilizingthe plurality of calibration points, or data derived therefrom, thetrained machine learning model receives as inputs at least one currentauxiliary sensor value and outputs at least one of a set of calibrationpoints or transform parameters. The at least one processor may optimizeat least one transform utilizing the trained machine learning model. Theat least one processor may receive a plurality of calibration pointsfrom the WHUD and from a population of WHUDs operated by a population ofusers.

A method of operating a wearable heads-up display device (WHUD)comprising a display and a glint detection module may be summarized asincluding receiving, by at least one processor, a glint space point in aglint space captured by the glint detection module, the glint spacepoint representative of a position of an eye of a user of the WHUD; fromtime-to-time during regular operation of the WHUD by the user,determining, by the at least one processor, a gaze location in a displayspace of the display based on the received glint space point and one ormore calibration point models, each of the one or more calibration pointmodels comprising a plurality of calibration points, each calibrationpoint including a glint space point; and a display space point in thedisplay space of the display, the display space point representative ofa location on the display of a user interface (UI) element displayed onthe display; and dynamically generating one or more child calibrationpoint models by, for each calibration point model: generating, by the atleast one processor, one or more additional calibration points; andadding, by the at least one processor, the one or more additionalcalibration points to the calibration point model. Generating the one ormore additional calibration points may include generating an additionalinferred calibration point. Generating the one or more additionalcalibration points may include generating a selected calibration point.

The method may further include evicting a calibration point from the oneor more calibration point models. Evicting a calibration point from theone or more calibration point models may include evicting an oldestcalibration point from the one or more calibration point models.Evicting a calibration point from the one or more calibration pointmodels may include evicting a calibration point based on at least one ofthe locations of calibration points in the one or more calibration pointmodels or the times at which the calibration points in the one or morecalibration point models were obtained.

The method may further include generating, by the at least oneprocessor, a transform from the glint space to the display space foreach of the one or more calibration point models. Generating a transformmay include generating an affine transform from the glint space to thedisplay space. Generating a transform may include solving a matrixutilizing at least one of a QR decomposition method or singular valuedecomposition method. The method may further include receiving, by theat least one processor, at least one auxiliary sensor value from atleast one auxiliary sensor during regular operation of the WHUD by theuser; and optimizing, by the at least one processor, the transform fromthe glint space to the display space for each of the one or morecalibration point models based at least in part on the received at leastone auxiliary sensor value. Receiving at least one auxiliary sensorvalue may include obtaining at least one auxiliary sensor value from atleast one of a proximity sensor, a gyroscope sensor or an accelerometer.

Each calibration point may further include at least one auxiliary sensorvalue from at least one auxiliary sensor obtained concurrently with theglint space point and the display space point, and the method mayfurther include, for each of the one or more calibration point models,training a machine learning model utilizing the plurality of calibrationpoints, or data derived therefrom, wherein the trained machine learningmodel receives as inputs at least one current auxiliary sensor value andoutputs a set of calibration points.

A wearable heads-up display (WHUD) may be summarized as including asupport frame; a display carried by the support frame; a glint detectionmodule carried by the support frame that, in operation, determines glintspace points in a glint space that correspond to a region in a field ofview of an eye of a user at which a gaze of the eye is directed; atleast one processor carried by the support frame, the at least oneprocessor communicatively coupled to the display and the glint detectionmodule; and at least one nontransitory processor-readable storage mediumcarried by the support frame, the at least one nontransitoryprocessor-readable storage medium communicatively coupled to the atleast one processor, wherein the at least one nontransitoryprocessor-readable storage medium stores data or processor-executableinstructions that, when executed by the at least one processor, causethe at least one processor to: receive a glint space point in a glintspace captured by the glint detection module, the glint space pointrepresentative of a position of an eye of a user of the WHUD; fromtime-to-time during regular operation of the WHUD by the user, determinea gaze location in a display space of the display based on the receivedglint space point and one or more calibration point models, each of theone or more calibration point models comprising a plurality ofcalibration points, each calibration point comprising: a glint spacepoint; and a display space point in the display space of the display,the display space point representative of a location on the display of auser interface (UI) element displayed on the display; and dynamicallygenerating one or more child calibration point models, wherein todynamically generate one or more child calibration point models, the atleast one processor, for each calibration point model: generates one ormore additional calibration points; and adds the one or more additionalcalibration points to the calibration point model. The additionalcalibration point may include an additional inferred calibration point.The additional calibration point may include a selected calibrationpoint. The at least one processor may evict a calibration point from theone or more calibration point models. For example, the at least oneprocessor may evict an oldest calibration point from the one or morecalibration point models, and/or the at least one processor may evict acalibration point based on at least one of the locations of calibrationpoints in the one or more calibration point models or the times at whichthe calibration points in the one or more calibration point models wereobtained.

The at least one processor may generate a transform from the glint spaceto the display space for each of the one or more calibration pointmodels. The at least one processor may generate an affine transform fromthe glint space to the display space. The at least one processor maysolve a matrix utilizing at least one of a QR decomposition method orsingular value decomposition method. The at least one processor mayreceive at least one auxiliary sensor value from at least one auxiliarysensor during regular operation of the WHUD by the user, and optimizethe transform from the glint space to the display space for each of theone or more calibration point models based at least in part on thereceived at least one auxiliary sensor value.

The WHUD may further include at least one auxiliary sensor selected froma group consisting of: a proximity sensor, a gyroscope sensor, and anaccelerometer. For each of the one or more calibration point models, theat least one processor may train a machine learning model utilizing theplurality of calibration points, or data derived therefrom, wherein thetrained machine learning model receives as inputs at least one currentauxiliary sensor value and outputs a set of calibration points.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, identical reference numbers identify similar elementsor acts. The sizes and relative positions of elements in the drawingsare not necessarily drawn to scale. For example, the shapes of variouselements and angles are not necessarily drawn to scale, and some ofthese elements may be arbitrarily enlarged and positioned to improvedrawing legibility. Further, the particular shapes of the elements asdrawn, are not necessarily intended to convey any information regardingthe actual shape of the particular elements, and may have been solelyselected for ease of recognition in the drawings.

FIG. 1 is an illustrative diagram showing a side view of a wearableheads-up display, in accordance with the present systems, devices, andmethods.

FIG. 2 is a perspective view of a wearable heads-up display, inaccordance with the present systems, devices, and methods.

FIG. 3 is a flowchart for a method of operation of an eye trackingsystem of a wearable heads-up display to perform an explicit calibrationprocess, in accordance with the present systems, devices, and methods.

FIG. 4 is a flowchart for a method of operation of an eye trackingsystem of a wearable heads-up display to perform an explicit 1-pointre-centering of an explicit calibration process, in accordance with thepresent systems, devices, and methods.

FIG. 5 is a functional block diagram of an eye tracking system of awearable heads-up display that utilizes a machine learning model toimprove a dynamic calibration scheme, in accordance with the presentsystems, devices, and methods.

FIG. 6 is a flowchart for a method of operation of an eye trackingsystem of a wearable heads-up display to obtain training data and totrain one or more machine learning models using the obtained trainingdata, in accordance with the present systems, devices, and methods.

FIG. 7 depicts a display of a wearable heads-up display, and shows anumber of calibration points obtained while a UI element moves in arectangular pattern around the perimeter of the display, in accordancewith the present systems, devices, and methods.

FIG. 8 depicts a display of a wearable heads-up display, and shows anumber of calibration points obtained while a UI element moves in arectangular pattern around the perimeter of the display, with thecalibration points shifted around the perimeter of the display withrespect to the calibration points shown in FIG. 7, in accordance withthe present systems, devices, and methods.

FIG. 9 is a flowchart for a method of operation of an eye trackingsystem of a wearable heads-up display that implements a dynamiccalibration scheme, in accordance with the present systems, devices, andmethods.

DETAILED DESCRIPTION

In the following description, certain specific details are set forth inorder to provide a thorough understanding of various disclosedimplementations. However, one skilled in the relevant art will recognizethat implementations may be practiced without one or more of thesespecific details, or with other methods, components, materials, etc. Inother instances, well-known structures associated with computer systems,server computers, and/or communications networks have not been shown ordescribed in detail to avoid unnecessarily obscuring descriptions of theimplementations.

Unless the context requires otherwise, throughout the specification andclaims that follow, the word “comprising” is synonymous with“including,” and is inclusive or open-ended (i.e., does not excludeadditional, unrecited elements or method acts).

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrases “in one implementation” or “in an implementation” in variousplaces throughout this specification are not necessarily all referringto the same implementation. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more implementations.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contextclearly dictates otherwise. It should also be noted that the term “or”is generally employed in its sense including “and/or” unless the contextclearly dictates otherwise.

The headings and Abstract of the Disclosure provided herein are forconvenience only and do not interpret the scope or meaning of theimplementations.

The various implementations described herein provide systems, devices,and methods for laser eye tracking in wearable heads-up displays. Morespecifically, the various implementations described herein providemethods of determining the gaze direction of an eye of a user and areparticularly well-suited for use in wearable heads-up displays (“WHUDs”)that employ scanning laser projectors (“SLPs”). Examples of WHUDsystems, devices, and methods that are particularly well-suited for usein conjunction with the present systems, devices, and methods for lasereye tracking are described in, for example, U.S. Non-Provisional patentapplication Ser. No. 15/167,458, U.S. Non-Provisional patent applicationSer. No. 15/167,472, U.S. Non-Provisional patent application Ser. No.15/167,484, and U.S. Non-Provisional patent application Ser. No.15/331,204.

Initially, an example method of determining a gaze direction of an eyeof a user of a WHUD is provided. Next, various example WHUDs arediscussed with regard to FIGS. 1 and 2. Then, various static and dynamiccalibration systems, methods and articles of the present disclosure arediscussed with reference to FIGS. 3-9.

Method of Eye Tracking

For the purpose of the following discussion, the term “user” refers to aperson that is operating and/or wearing the hardware elements describedbelow (e.g., a person that is wearing a wearable heads-up display, asdescribed in more detail below).

In at least some implementations, an eye tracking system of a WHUD mayinclude a “glint detection module” that includes at least one infraredlaser diode, at least one scan mirror, at least one infrared sensor, atleast one processor, and at least one nontransitory processor-readablestorage medium that stores at least one of instructions or data that,when executed by the at least one processor, cause the WHUD to implementthe functionality discussed below. Examples of glint detection modulesare shown in FIGS. 1 and 2.

In at least some implementations, the infrared laser diode may generateinfrared laser light. Depending on the specific implementation, theinfrared laser diode may activate and remain active in order tocontinuously generate a continuous beam of infrared laser light, or theinfrared laser diode may be modulated to generate a sequence or patternof infrared laser light. Throughout this specification and the appendedclaims, the term “infrared” includes “near infrared” and generallyrefers to a wavelength of light that is larger than the largestwavelength of light that is typically visible to the average human eye.Light that is visible to the average human eye (i.e., “visible light”herein) is generally in the range of 400 nm-700 nm, so as used hereinthe term “infrared” refers to a wavelength that is greater than 700 nm,up to 1 mm. As used herein and in the claims, visible means that thelight includes wavelengths within the human visible portion of theelectromagnetic spectrum, typically from approximately 400 nm (violet)to approximately 700 nm (red).

The at least one scan mirror may scan the infrared laser light over theeye of the user. Depending on the modulation of the infrared laserdiode, the at least one scan mirror may scan the infrared laser lightover (e.g., completely illuminate) a substantially continuous surface ofthe eye or the at least one scan mirror may scan the infrared laserlight to form an illumination pattern on the surface of the eye (such asa grid pattern, a crosshairs pattern, and so on). Generally, in orderfor the at least one scan mirror to scan the infrared laser light overthe eye of the user, the at least one scan mirror may sweep through arange of orientations and, for a plurality of orientations of the atleast one scan mirror (i.e., for each respective orientation of the atleast one scan mirror if the infrared laser diode is continuously activein order to completely illuminate the corresponding surface of the eye,or for a subset of orientations of the at least one scan mirror if theinfrared laser diode is modulated such that the combination of subsetsof orientations of the at least one scan mirror and the modulationpattern of the infrared laser diode produces an illumination pattern onthe corresponding surface of the eye), the at least one scan mirror mayreceive the infrared laser light from the infrared laser diode andreflect the infrared laser light towards a respective region of the eyeof the user.

The at least one scan mirror may include one or multiple (e.g., in a DLPconfiguration) digital microelectromechanical systems (“MEMS”) mirror(s)or one or multiple piezoelectric mirrors.

In some implementations, the at least one scan mirror may scan infraredlaser light directly over at least a portion of the eye of the user. Inother implementations (e.g., in applications in which eye tracking isperformed by a scanning laser-based WHUD), the at least one scan mirrormay indirectly scan infrared laser light over at least a portion of theeye of the user by scanning the infrared laser light over an area of alight-redirection element (such as a holographic optical element(“HOE”), a diffraction grating, a mirror, a partial mirror, or awaveguide structure) positioned in the field of view of the eye of theuser and the light-redirection element may redirect the infrared laserlight towards the eye of the user. In implementations that employ suchindirect scanning, the light-redirection element (e.g., the HOE) may,upon redirection of the infrared laser light towards the eye of theuser, converge the infrared laser light to an exit pupil at the eye ofthe user, where the exit pupil encompasses at least the cornea of theeye of the user (when the user is looking in a specific direction, suchas straight ahead or straight towards display content displayed by aWHUD).

Reflections of the infrared laser light from the eye of the user aredetected by the at least one infrared sensor, such as an infrareddetector or, more specifically, an infrared photodetector. As will bediscussed in more detail below, the at least one infrared sensor may becommunicatively coupled to a processor (e.g., a digital processor, or anapplication-specific integrated circuit) and provide an output signalhaving a magnitude that depends on an intensity of the infrared laserlight detected by the infrared sensor.

The at least one processor communicatively coupled to the at least oneinfrared sensor may determine a respective intensity of a plurality ofthe reflections of the infrared laser light detected by the infraredsensor (i.e., “detected reflections”). The percentage of detectedreflections for which the processor determines an intensity may dependon, for example, the sampling rate of the processor. The “intensity” ofa detected reflection may be a measure of, for example, the brightnessof the detected reflection, the luminance of the detected reflection,and/or the power of the detected reflection.

The processor may identify at least one detected reflection for whichthe intensity exceeds a threshold value. Generally, the at least oneinfrared sensor may be oriented to detect both spectral and diffusereflections of the infrared laser light from the eye of the user;however, in some implementations the processor may specifically identifya detected reflection for which the intensity exceeds a threshold valueonly when the infrared sensor detects a spectral reflection of theinfrared laser light from the eye of the user. Such spectral reflectionmay, for example, correspond to the cornea reflection, first Purkinjeimage, or “glint.”

As previously described, the processor may sample the signal output bythe at least one infrared sensor, where the magnitude of the signal (andtherefore the magnitude of each sample) depends on the intensity of theinfrared laser light detected by the at least one infrared sensor. Inthis case, the processor may identify at least one detected reflectionfor which the intensity exceeds a threshold value by identifying a firstsample (in a series of samples) for which the magnitude exceeds athreshold magnitude. In other words, identifying, by the processor, atleast one detected reflection for which the intensity exceeds athreshold value may be an edge-triggered (e.g., rising edge-triggered)process. If desired, the processor may then continue to identify thatsubsequent detected reflections each have intensities that do exceed thethreshold until the processor identifies a second sample in the seriesfor which the magnitude does not exceed the threshold magnitude (e.g., afalling edge-triggered process).

The processor may determine the orientation of the at least one scanmirror that corresponds to the at least one detected reflection forwhich the intensity exceeds the threshold value. In other words, theprocessor determines which orientation of the at least one scan mirrorcaused the infrared laser light to reflect from the eye of the user, asdetected, with an intensity that exceeds the determined threshold value.

The processor may determine a region in a field of view of the eye ofthe user at which a gaze of the eye is directed based on the orientationof the at least one scan mirror that corresponds to the at least onedetected reflection for which the intensity exceeds the determinedthreshold value. Generally, this may include effecting, by theprocessor, a mapping between the orientation of the at least one scanmirror that corresponds to the at least one detected reflection forwhich the intensity exceeds the threshold value and the field of view ofthe eye of the user.

As an example, the processor may essentially effect a mapping between“detected reflection space” and “mirror orientation space” which, sinceonly detected reflections that exceed the threshold value are ofinterest and since detected reflections that exceed the threshold valuemay generally be “glints,” may be interpreted as a mapping between“glint space” and “mirror orientation space.” Then, the processor mayessentially effect a mapping between “mirror orientation space” and gazedirection of the eye based on established correlations between variousmirror orientations and where the corresponding infrared laser lightwould appear in the user's field of view (e.g., if redirected by alight-redirection element such as an HOE positioned in the user's fieldof view) if the infrared laser light was visible to the user. In thisway, the method may essentially effect a mapping between “glint space”and “gaze direction space.”

Fundamentally, the processor may effect a mapping between theorientation of the at least one scan mirror that corresponds to the atleast one detected reflection for which the intensity exceeds thethreshold value (e.g., “glint space”) and the field of view of the eyeof the user (e.g., “field of view space”) or a location on a display ofthe WHUD (e.g., “display space”) by performing at least onetransformation between a set of scan mirror orientations and a set ofgaze directions of the eye of the user. Non-limiting examples of the atleast one transformation include a linear transformation, a geometrictransformation, an affine transformation, a neural network-basedtransformation, etc. In various implementations, such may include anynumber of intervening transformations, such as a transformation fromglint space to a set of display coordinates and a transformation fromthe set of display coordinates to the set of gaze directions of the eyeof the user.

Depending on the specific implementation, the at least one scan mirrormay include a single scan mirror that is controllably orientable abouttwo orthogonal axes or two scan mirrors that are each respectivelycontrollable about a respective axis, with the respective axes aboutwhich the two scan mirrors are controllably orientable being orthogonalto one another. For example, a single scan mirror may scan the infraredlaser light over two dimensions of the user's eye, or a first scanmirror may scan the infrared laser light across a first dimension of theeye and a second scan mirror may scan the infrared laser light across asecond dimension of the eye. The at least one scan mirror may sweepthrough a range of orientations. In the case of two orthogonal scanmirrors, this may mean that a first scan mirror sweeps through a firstrange of orientations and, for each respective orientation of the firstscan mirror, a second scan mirror sweeps through a second range oforientations. It follows that, for a plurality of orientations of the atleast one scan mirror, the at least one scan mirror receives theinfrared laser light from the infrared laser diode and reflects theinfrared laser light towards a respective region of the eye of the user,with two orthogonal scan mirrors the infrared laser light is reflectedtowards a respective region of the eye of the user for each respectivecombination of a first orientation of the first scan mirror and a secondorientation of the second scan mirror. Furthermore, with two orthogonalscan mirrors the processor may determine the combination of the firstorientation of the first scan mirror and the second orientation of thesecond scan mirror that corresponds to the at least one detectedreflection for which the intensity exceeds the threshold value and theprocessor may determine the region in the field of view of the eye ofthe user at which the gaze of the eye is directed based on thecombination of the first orientation of the first scan mirror and thesecond orientation of the second scan mirror that corresponds to the atleast one detected reflection for which the intensity exceeds thethreshold value.

As previously described, the method may be particularly advantageouswhen implemented in a WHUD that employs a SLP because in such animplementation the eye tracking (i.e., gaze direction detection)functionality of the method may be achieved with minimal hardwareadditions (and correspondingly minimal bulk and impact on aestheticdesign) to the WHUD. In this case, the method may be extended to includea projection of display content to the user and a determination of wherein the display content the user's gaze is directed.

For example, the infrared laser diode and the at least one scan mirrorof the method may be components of a SLP, and the SLP may furtherinclude at least one additional laser diode to generate visible laserlight. In this case, the method may be extended to include projectingvisible display content in the field of view of the eye of the user bythe SLP and the processor may determine a region of the visible displaycontent at which the gaze of the eye is directed based on theorientation of the at least one scan mirror that corresponds to the atleast one detected reflection for which the intensity exceeds thethreshold value. The processor may determine a region of the visibledisplay content at which the gaze of the eye is directed by performing atransformation between a set of scan mirror orientations and a set ofregions of the visible display content. In other words, the processormay effect a mapping between “mirror orientation space” (or “glintspace,” as previously described) and “display space.”

Example WHUDs

FIG. 1 is an illustrative diagram showing a WHUD 100 that includes a SLP110 with an integrated eye tracking functionality in accordance with thepresent systems, devices, and methods. In brief, WHUD 100 includes theSLP 110 which is adapted to include an infrared laser diode (labeled as“IR” in FIG. 1) for eye tracking purposes and a transparent combinercomprising a wavelength-multiplexed HOE 130 integrated with (e.g.,laminated or otherwise layered upon, or cast within) an eyeglass lens160. Integration of HOE 130 with lens 160 may include and/or employ thesystems, devices, and methods described in U.S. Non-Provisional patentapplication Ser. No. 15/256,148 and/or U.S. Provisional PatentApplication Ser. No. 62/268,892.

In WHUD 100, scanning laser projection and eye tracking components areboth integrated into a single package/module 110. Specifically, SLP 110comprises a laser module 111 that includes red laser diode (labelled “R”in FIG. 1), a green laser diode (labelled “G” in FIG. 1), and a bluelaser diode (labelled “B” in FIG. 1) and a scan mirror 112 (a singlemirror illustrated for simplicity, though as previously described atleast two orthogonally-orientable mirrors may be used). In addition,laser module 111 also includes an infrared laser diode (labelled “IR” inFIG. 1) for use in eye tracking/gaze detection. Scan mirror 112simultaneously serves as both the scan mirror for laser projection and ascan mirror for eye tracking, whereby scan mirror 112 scans infraredlaser light (represented by dashed lines 122 in FIG. 1) over the area ofeye 190 to sequentially illuminate an area of eye 190 (e.g., via araster scan of IR light). In WHUD 100, infrared laser diode isintegrated into laser module 111 of SLP 110 and scan mirror 112 servesto scan both visible (R, G, and/or B) and infrared (IR) laser light overeye 190.

Scan mirror 112 may advantageously include one or multiple (e.g., in aDLP configuration) digital microelectromechanical systems (“MEMS”)mirror(s). In typical operation, scan mirror 112 of SLP 110 repeatedlyscans over its entire range of orientations and effectively scans overthe entire field of view of the display. Whether or not an image/pixelis projected at each scan orientation depends on controlled modulationof laser module 111 and its synchronization with scan mirror 112. Thefact that scan mirror 112 generally scans over its entire range duringoperation as a laser projector makes scan mirror 112 of SLP 110compatible with use for eye tracking purposes. SLP 110 is adapted toprovide eye tracking functionality without having to compromise ormodify its operation as a SLP. In operation, scan mirror 112 repeatedlyscans over its entire range of orientations while the RGB laser diodesare modulated to provide the visible light 121 corresponding to pixelsof a scanned image or, generally, “display content.” At the same time,the infrared laser diode may be activated to illuminate the user's eye190 (one spot or pixel at a time, each corresponding to a respectivescan mirror orientation) with infrared laser light 122 for eye trackingpurposes. Depending on the implementation, the infrared laser diode maysimply be on at all times to completely illuminate (i.e., scan over theentire area of) eye 190 with infrared laser light 122 or the infraredlaser diode may be modulated to provide an illumination pattern (e.g., agrid, a set of parallel lines, a crosshair, or any other shape/pattern)on eye 190. Because infrared laser light 122 is invisible to eye 190 ofthe user, infrared laser light 122 does not interfere with the scannedimage being projected by SLP 110.

In order to detect the (e.g., portions of) infrared laser light 122 thatreflects from eye 190, WHUD 100 includes at least one infraredphotodetector 150. While only one photodetector 150 is depicted in FIG.1, in alternative implementations any number of photodetectors 150 maybe used (e.g., an array of photodetectors 150, or a charge-coupleddevice based camera that is responsive to light in the infraredwavelength range) positioned in any arrangements and at any desiredlocation(s) depending on the implementation.

As scan mirror 112 scans modulated R, G, and/or B light 121 over eye 190to produce display content based on modulation of the R, G, and/or Blaser diodes, scan mirror 112 also scans infrared laser light 122 overeye 190 based on modulation of the IR laser diode. Photodetector 150detects an intensity pattern or map of reflected infrared laser light122 that depends on the position/orientation of eye 190. That is, eachdistinct orientation of scan mirror 112 may result in a respectiveintensity of infrared laser light 122 being detected by photodetector150 that depends on the position/orientation of eye 190 (or theposition/orientation of feature(s) of eye 190, such as the cornea, iris,pupil, and so on). The intensity pattern/map detected by photodetector150 depends on where eye 190 is looking. In this way, the same SLP 110in WHUD 100 enables both i) image projection, and ii) the gaze directionand movements of eye 190 to be measured and tracked.

Another adaptation to WHUD 100, for the purpose of integrating eyetracking functionality into SLP 110, is wavelength-multiplexing of HOE130. WHUD 100 includes a HOE 130 that redirects laser light output fromthe laser module 111 of SLP 110 towards eye 190; however, in WHUD 100,HOE 130 includes at least two wavelength-multiplexed holograms: at leasta first hologram 131 that is responsive to (i.e., redirects at least aportion of, the magnitude of the portion depending on the playbackefficiency of the first hologram) the visible light 121 output by lasermodule 111 and unresponsive to (i.e., transmits) the infrared light 122output by laser module 111, and a second hologram 132 that is responsiveto (i.e., redirects at least a portion of, the magnitude of the portiondepending on the playback efficiency of the second hologram) theinfrared light 122 output by laser module 111 and unresponsive to (i.e.,transmits) the visible light 121 output by laser module 111. While FIG.1 depicts first hologram 131 as a single hologram, in practice theaspect(s) of HOE 130 that is/are responsive to the visible light 121output by laser module 111 may include any number of holograms that maybe multiplexed in a variety of different ways, including withoutlimitation: wavelength multiplexed (i.e., a “red” hologram that isresponsive to only red light from the red laser diode of laser module111, a “green” hologram that is responsive to only green light from thegreen laser diode of laser module 111, and a “blue” hologram that isresponsive to only blue light from the blue laser diode of laser module111), angle multiplexed (e.g., for the purpose of eye boxexpansion/replication), phase multiplexed, spatially multiplexed,temporally multiplexed, and so on. Upon redirection of visible light121, first hologram 131 may apply a first optical power to visible light121. Advantageously, the first optical power applied by first hologram131 (or by the first set of multiplexed holograms if the implementationemploys a set of multiplexed holograms for redirecting the visible light121) may be a positive optical power that focuses or converges thevisible light 121 to, for example, an exit pupil having a diameter lessthan one centimeter (e.g., 6 mm, 5 mm, 4 mm, 3 mm) at the eye 190 of theuser for the purpose of providing a clear and focused image with a widefield of view. Upon redirection of infrared light 122, second hologram132 may apply a second optical power to infrared light 122, where thesecond optical power applied by second hologram 132 is different fromthe first optical power applied by first hologram 131. Advantageously,the first optical power may be greater than the second optical power(and therefore, the second optical power may be less than the firstoptical power) so that second hologram 132 redirects infrared light 122over an area of eye 190 that is larger than the exit pupil of visiblelight 121 at eye 190. In other words, the first hologram that isresponsive to the visible light may converge the visible light to afirst exit pupil at the eye of the user and the second hologram that isresponsive to the infrared light may converge the infrared light to asecond exit pupil at the eye of the user, where the first exit pupil iscompletely contained within the second exit pupil at the eye of theuser. For example, the second optical power of second hologram 132 mayapply a rate of convergence to infrared light 122 that is less than therate of convergence applied to visible light 121 by the first opticalpower of first hologram 131, or the second optical power may be zerosuch that second hologram 132 redirects infrared light 122 towards eye190 without applying any convergence thereto, or the second opticalpower may be negative (i.e., less than zero) so that the second opticalpower of second hologram 132 causes infrared light 122 to diverge (i.e.,applies a rate of divergence thereto) to cover, for example, the entirearea of eye 190 (and beyond, if desired) for the purpose of illuminatinga large area of eye 190 and tracking all eye positions/motions withinthat illuminated area.

Depending on the specific implementation, HOE 130 may comprise a singlevolume of holographic material (e.g., photopolymer or a silver halidecompound) that encodes, carries, has embedded therein or thereon, orgenerally includes both first hologram 131 and second hologram 132, oralternatively HOE 130 may comprise at least two distinct layers ofholographic material (e.g., photopolymer and/or a silver halidecompound) that are laminated or generally layered together, a firstlayer of holographic material that includes first hologram 131 and asecond layer of holographic material that includes second hologram 132.

The use of infrared light is advantageous in eye tracking systemsbecause infrared light is invisible to the (average) human eye and sodoes not disrupt or interfere with other optical content being displayedto the user. Integrating an infrared laser diode into a SLP, inaccordance with the present systems, devices, and methods, enablesvisible laser projection and invisible eye tracking to be simultaneouslyperformed by substantially the same hardware of a WHUD, therebyminimizing overall bulk and processing/power requirements of the system.

In accordance with the present systems, devices, and methods, an eyetracking system (or an “eye tracker”) may include one or more digitalprocessor(s) communicatively coupled to the one or more infraredphotodetector(s) and to one or more non-transitory processor-readablestorage medium(ia) or memory(ies). The memory(ies) may storeprocessor-executable instructions and/or data that, when executed by theprocessor, enable the processor to determine the position and/or motionof an eye of the user, or the gaze direction of the eye of the user,based on information (e.g., intensity information, such as an intensitypattern/map) provided by the one or more photodetector(s).

FIG. 2 is a perspective view of a WHUD 200 that integrates eye trackingand scanning laser projection in accordance with the present systems,devices, and methods. WHUD 200 includes many of the elements depicted inFIG. 1, namely: an SLP 210 comprising laser module 211 with at least onevisible laser diode (e.g., a red laser diode, a green laser diode, ablue laser diode, or any combination thereof) to output a visible laserlight 221 (e.g., a red laser light, a green laser light, a blue laserlight, or any combination thereof) and an infrared laser diode to outputinfrared laser light 222, at least one scan mirror 212 aligned toreceive laser light output from the laser module 211 and controllablyorientable to reflect (i.e., scan) the laser light, awavelength-multiplexed HOE 230 aligned to redirect the laser light 221and 222 towards an eye 290 of a user, and at least one infraredphotodetector 250 responsive to infrared laser light 222. Depending onthe implementation, the visible laser light 221 may correspond to anyof, either alone or in any combination, a red laser light, a green laserlight, and/or a blue laser light. WHUD 200 also includes a support frame280 that has a general shape and appearance or a pair of eyeglasses.Support frame 280 carries SLP 210, photodetector 250, andwavelength-multiplexed HOE 230 so that HOE 230 is positioned within afield of view of the eye 290 of the user when support frame 280 is wornon a head of the user.

Support frame 280 of WHUD 200 also carries a digital processor 260communicatively coupled to SLP 210 and photodetector 250, and anon-transitory processor-readable storage medium or memory 270communicatively coupled to digital processor 270. Memory 270 stores dataand/or processor-executable instructions 271 that, when executed byprocessor 260, cause WHUD 200 to perform the functionality discussedherein. More specifically, data and/or processor-executable instructions271, when executed by processor 260, cause WHUD 200 to: generate aninfrared laser light 222 by the infrared laser diode of SLP 210; scanthe infrared laser light 222 over the eye 290 of the user by the atleast one scan mirror 212, wherein scanning the infrared laser light 222over the eye 290 of the user by the at least one scan mirror 212includes sweeping the at least one scan mirror 212 through a range oforientations and, for a plurality of orientations of the at least onescan mirror 212, reflecting the infrared laser light 222 towards arespective region of the eye 290 of the user; detect reflections 223 ofthe infrared laser light 222 from the eye 290 of the user by the atleast one infrared photodetector 250; determine a respective intensityof each detected reflection 223 of the infrared laser light 222 by theprocessor 260; identify, by the processor 260, at least one detectedreflection 223 for which the intensity exceeds a threshold value;determine, by the processor 260, the orientation of the at least onescan mirror 212 that corresponds to the at least one detected reflection223 for which the intensity exceeds the threshold value; and determine,by the processor 260, a region in a field of view of the eye 290 of theuser at which a gaze of the eye 290 is directed based on the orientationof the at least one scan mirror 212 that corresponds to the at least onedetected reflection 223 for which the intensity exceeds the thresholdvalue. Together, all of these acts enable WHUD 200 to determine a gazedirection of eye 290.

Since, in addition to eye tracking/gaze direction detection capability,WHUD 200 also has a display capability, memory 270 further stores dataand/or processor-executable instructions that, when executed byprocessor 260, cause WHUD 200 to project visible display content 231 inthe field of view of the eye 290 of the user by SLP 210 (in conjunctionwith HOE 230). In this case, data and/or processor-executableinstructions 271, when executed by processor 260, may cause WHUD 200 todetermine, by the processor 260, a region in a field of view of the eye290 of the user at which a gaze of the eye 290 is directed based on theorientation of the at least one scan mirror 212 that corresponds to theat least one detected reflection 223 for which the intensity exceeds thethreshold value, by causing WHUD 200 to determine, by the processor 260,a region of the visible display content 231 at which the gaze of the eye290 is directed based on the orientation of the at least one scan mirror212 that corresponds to the at least one detected reflection 223 forwhich the intensity exceeds the threshold value.

As previously described, infrared photodetector 250 may advantageouslybe positioned on support frame 280 at a periphery of the field of viewof the eye 290 of the user when the eye 290 is gazing straight ahead(e.g., on the rims of frame 280 that surround the eyeglass lens thatcarries HOE 230). In this case, the data and/or processor-executableinstructions that, when executed by the processor 260, cause WHUD 200 toproject visible display content 231 in the field of view of the eye 290of the user by the SLP 210, may advantageously cause the SLP 210 toposition the visible display content 231 away-from-center in the fieldof view of the eye 290 of the user and towards the position of the atleast one infrared photodetector 250 at the periphery of the field ofview of the eye 290 of the user, as depicted in the exemplaryimplementation of FIG. 2.

In at least some implementations, the WHUD 200 may include one moreauxiliary sensors 292 carried by the support frame 280 at one or morelocations thereof. Non-limiting examples of such auxiliary sensorsinclude proximity sensors, gyroscopes or accelerometers. A proximitysensor may be used to indicate where the support frame 280 is positionedrelative to a “home” position on a user's face (e.g., distance from theproximity sensor to a point on the user's face, such as the nosebridge), which provides information about where the support frame 280 islocated relative to the eye(s) of the user. A gyroscope sensor may beused to determine the orientation of a user's head during use of theWHUD 200. An accelerometer may be used to indicate patterns ofacceleration, including sudden movements, that are occurring as the userwears the WHUD. As discussed further below, in at least someimplementations auxiliary sensors may be used to improve calibrationmethods for the WHUD 200.

Throughout this specification, Figures, as well as the appended claims,reference is often made to the eye of the user. For example, FIG. 1depicts eye 190, and FIG. 2 depicts eye 290. In general, the systems,devices, and methods described herein are suitable for use inassociation with at least one eye of a user (e.g., 190, 290) but do notthemselves include the eye of the user. In other words, eye 190 is not apart of WHUD 100 and eye 290 is not a part of WHUD 200.

The various implementations described herein generally reference andillustrate a single eye of a user (i.e., monocular applications), but aperson of skill in the art will readily appreciate that the presentsystems, devices, and methods may be duplicated in a WHUD in order toprovide scanned laser projection and/or scanned laser eye tracking forboth eyes of the user (i.e., binocular applications).

The various implementations described herein measure, sense, detect,identify, or otherwise determine the intensity of detected infraredreflections and use this information to identify when the intensity of adetected infrared reflection exceeds a threshold value. The thresholdvalue may be a certain percentage above a baseline detection value, suchas 10% above, 50% above, 100% above, 500% above, 1000% above, or so ondepending on the specific implementation. A detected infrared reflectionthat exceeds the threshold value is used herein because such generallycorresponds to a spectral reflection for the eye of the user known asthe first Purkinje image or glint. The glint provides a useful,reliable, and sufficient detection feature for the purpose ofdetermining the relative gaze direction of the eye of the user; thus, inmethod only detected reflections that correspond to glints are used todetermine the gaze direction of the eye of the user. However, the entirecollection of detected reflection intensities of the infrared laserlight from the eye of the user can be useful in other applications. Forexample, the method may be employed to produce a complete (depending onthe resolution given, at least in part, by the step size betweenorientations of the at least one scan mirror) infrared image of the eyeof the user. This infrared image may be used for more detailed (and morecomputational intensive) eye tracking and gaze detection purposes, orfor other purposes such as user authentication via iris or retinal bloodvessel recognition. That is, conventional techniques and algorithms foriris recognition and/or retinal blood vessel recognition (whichtypically use visible light and color photography or videography) may beadapted to employ scanned infrared laser light and infrared images ofthe eye of the user generated by performing acts of the method (togetherwith further acts of data processing to produce an infrared image andimage processing to achieve recognition).

The various implementations of eye tracking systems and devicesdescribed herein may, in some implementations, make use of additional oralternative “Purkinje images” (i.e., other than the “glint”) and/or mayemploy the “corneal shadow” based methods of eye tracking described inU.S. Non-Provisional patent application Ser. No. 15/331,204.

The WHUDs described herein may include one or more sensor(s) (e.g.,microphone, camera, thermometer, compass, and/or others) for collectingdata from the user's environment. For example, one or more camera(s) maybe used to provide feedback to the processor of the wearable heads-updisplay and influence where on the transparent display(s) any givenimage should be displayed.

The WHUDs described herein may include one or more on-board powersources (e.g., one or more battery(ies)), a wireless transceiver forsending/receiving wireless communications, and/or a tethered connectorport for coupling to a computer and/or charging the one or more on-boardpower source(s).

Calibration Systems, Devices, and Methods

The following discussion provides example implementations of variouscalibration systems, methods and articles for eye tracking systems ofWHUDs, such as the WHUDs 100 and 200 discussed above with reference toFIGS. 1 and 2, respectively.

Generally, the present disclosure provides various calibrationapproaches that may be used for an eye tracking system of a WHUD. Asdiscussed further below, one approach includes a comprehensive initialexplicit phase (e.g., required after unboxing of a WHUD by a user) togather information and train user-specific machine learning models toassist with eye tracking and calibration. Example implementations of animplicit calibration phase that utilizes a user action that initiates aninteraction with the WHUD are also provided. Additionally,implementations of a dynamic, implicit calibration during aninteraction, using known user interface (UI) geometry, are provided.

According to one or more implementations, a minimally-obtrusive explicitcalibration phase may be provided that has several features. Forexample, the explicit calibration phase may be initiated when the WHUDdetects that the tolerance of movement, discussed below, is beingapproached or has been abruptly exceeded. The calibration phase may makethe user aware that the WHUD must be returned to a home position on theuser's head, and may await confirmation that the WHUD has been returnedto the home position. The WHUD may then perform implicit calibration(e.g., without requiring deliberate user input, without requiring userknowledge of the calibration) so the user may resume the interactionwith the WHUD. According to at least some implementations, a dynamiccalibration system may provide continuous user and population learningto optimize user-specific machine learning models that assist with eyetracking and calibration.

Recent simulations by Applicant have shown that when a support frame ofa WHUD moves down the nose of a user by 0.1 millimeters (mm), 1° oferror may be introduced into the inferred gaze position. Recentexperiments have also shown that significant nasal skin movement occursduring head movement. Since the display of a WHUD may span only 10°, forexample, a movement of 0.5 mm introduces error such that when the useris looking at one half of the display, the cursor location may beinferred to be in the other half of the display, or even significantlyoutside of the display. Without calibration after such a movement, asimple binary choice in the UI, where each choice occupies half of thedisplay, may not be possible. Accordingly, eye-tracking in WHUDs is verysensitive to movement of the WHUD with respect to the eye. Movements ofa fraction of a millimeter can cause significant changes to the inferredgaze position, necessitating calibration.

A “home position” may be defined as the optimal snug position of thesupport frame of a WHUD manufactured for a given user. In practice, whenmovement of the support frame from the home position occurs (e.g.,abruptly or over time), there exists a threshold where eye trackingerror and/or calibration error will become too large for the UI to bereliably usable. This factor may be defined as the tolerance ofmovement.

Tolerance of movement is maximized when the effects of movement areminimized. As tolerance of movement increases, user experience improvesdrastically, and the need for glasses adjustment and calibration isreduced. Common use-cases that inherently involve movement (e.g.,talking, walking, running, riding in a vehicle) become possible, and UIgranularity can be increased significantly. Thus, WHUDs should have thelargest possible tolerance of movement. User experience improvesdrastically as tolerance of movement increases.

The following discussion provides various approaches to calibration ofthe relationship between the user's eye position and the user's gazelocation on the display of the WHUD to minimize the effects of physicalmovement of the support frame with respect to the user. As the toleranceof movement for accurate eye tracking increases, the frequency ofrequired support frame adjustments, which causes interruption,decreases. Additionally, as eye tracking accuracy within this toleranceof movement increases, the accuracy of each calibration increases. Thisimproves cursor accuracy, which decreases the number of subsequentcalibrations that are needed and improves the accuracy of eachcalibration. Therefore, accurate and robust eye tracking is important tominimizing the effects of physical movement of the support frame withrespect to the user.

In at least some implementations, a WHUD of the present disclosure mayimplement an implicit calibration scheme at interaction start. When thetolerance of movement is approached or abruptly exceeded, and thecurrent interaction has been interrupted to allow the user to return thesupport frame to the home position, calibration may be necessary beforethe interaction can resume. Calibration may also be beneficial at thebeginning of each interaction, to provide the best possible startingpoint for subsequent dynamic implicit calibration, discussed furtherbelow.

It may be desirable to avoid explicit calibration during everyday use ofthe WHUD. However, explicit calibration is generally much more reliablethan implicit calibration, as the target of the user's gaze can be knownwith high confidence since the user selects one or more UI elements atknown locations, as opposed to the user's gaze being inferred with lowerconfidence. It may be beneficial if an implicit calibration scheme withthe reliability and accuracy of an explicit calibration scheme could beperformed at the beginning of each interaction. The following discussionprovides systems and methods to achieve this while offering a good userexperience.

In at least some implementations, user action to initiate an interactionmay double as a calibration action, thus making explicit calibrationimplicit. An interaction with the WHUD may begin in a consistent way,akin to beginning an interaction with a smartphone. An interaction mayalso be prevented from beginning unintentionally, akin to a smartphone'sswipe-to-unlock feature. Such features create a user experience atinteraction start that is very familiar to smartphone users, while alsodisguising explicit calibration, rendering it implicit. The following isan example implementation of an interaction start action.

A user may click a user interface device (e.g., an interface on theWHUD, a companion ring) to wake the display of the WHUD. At least oneprocessor of the WHUD may show an unlock screen on the display thatincludes a small, personalizable UI element (e.g., logo). This UIelement may move at a medium speed (e.g., from left to right across thedisplay, in a rectangular pattern, in a circular pattern). This motionmay be as smooth and unobtrusive as possible. The WHUD may rapidlydetect that the user's eyes are following the UI element, and gatherenough information to perform a calibration. This can be accomplished ina fraction of the time that it takes for the UI element to move acrossthe display.

If no attempt to follow the UI element is made, or if calibration wasnot successful, the display may be controlled to enter a sleep mode. Peruser customization, or if the WHUD deems that past calibration behaviorhas not been adequately robust, a more complex variant of the unlockscreen may be shown (e.g., L-pattern, triangle pattern). There are manycreative and functional possibilities, including providingpersonalization and security options.

As the WHUD detects that the user is becoming more experienced with theinteraction start action, the speed of the UI element may increasegradually (to an upper bound), to reduce the overall time needed toexecute the action. In at least some implementations, on average, theinteraction start action may take under 0.5 seconds. This approach hasthe benefit of using a familiar click and swipe unlock action that willgrab users' attention right away. Additionally, using this approach,unintentional clicks can be seamlessly ignored. A further advantage ofthis approach is that it hides explicit calibration, rendering itimplicit. Further, calibration against a moving UI element significantlyimproves confidence in the calibration, as opposed to a static,one-point calibration. By providing an object to follow, smooth pursuiteye movement is possible, which allows many additional data points to begathered in a short amount of time.

In at least some implementations, performing a calibration atinteraction start may not be necessary. In such implementations, theinteraction start action can be streamlined.

In at least some implementations, approach or breach of the tolerance ofmovement may require a reset action to resume interaction. When thetolerance of movement is being approached or has been abruptly exceeded,a “calibration reset” is preferable, to avoid the user losing confidencein the robustness of the UI. The following discussion provides anexample implementation of a reset action.

When the WHUD detects that the tolerance of movement is being approachedor has been abruptly exceeded, the display may be put into a sleep mode,for example. As another non-limiting example, a subtle indication in theUI to return the glasses to the “home position” may be presented. Theuser will expect and understand this behavior, which cues the user toreturn the WHUD to the home position, and then the WHUD may execute theinteraction start action discussed above. Then, the current interactionis resumed.

This approach to the reset action offers a number of benefits. First,the reset action provides a seamless intervention before behavior of theUI becomes erratic, incorrect, or unresponsive. Second, the reset actioncues the user to make a simple (and potentially rewarding) adjustment ofthe support frame, and then initiates the interaction start action,which the user is already proficient at. The interaction start actionalready contains the implicit calibration that should be executed afterthe adjustment of the WHUD on the user's head.

The user may choose to return the support frame to the home positionwhile the support frame is still within the tolerance of movement. Thiscould be comfort-related, in anticipation that the tolerance of movementwill soon be approached, or in response to subtle eye trackingperformance degradation. In at least some implementations, the WHUDdetects this user action, and dynamic calibration attempts to seamlesslyrecover from this (possibly significant) change in physical position. Ifrecovery is not possible, the current interaction may be interrupted,and the interaction start action should be presented to resume theinteraction.

In at least some implementations, a proximity sensor may be used todetect a significant change in proximity. As another example, anoutward-facing sensor (e.g., in the same area as the proximity sensor)may detect the user's finger contact (e.g., pushing the glasses to thehome position). Such sensor may be used alone or in conjunction with theone or more inward-facing proximity sensors to improve robustness. Asanother example, an inertial measurement unit (IMU) may be used alone orin conjunction with one or both of the above-mentioned sensors.

In at least some implementations, the eye tracking system of a WHUD mayprovide dynamic implicit calibration during regular user interactionbased on eye movement history, selections (e.g., clicks) and known UIgeometry. Such information may be real-time, user-specific andcontext-specific, as opposed to historical, population-specific, andcontext-general. At a high level, dynamic implicit calibrationcontinuously infers the sequence of selectable UI elements that theuser's gaze has just traversed, then uses this information to update thecalibration, which is the estimated relationship between the user's eyeposition and the user's gaze location on the glasses display, inreal-time.

More complex optimizations, discussed further below, may be used to helpguide the dynamic implicit calibration process (e.g., predicting supportframe movement and calibration changes over time, calibration confidenceestimations).

In at least some implementations, the UI of a WHUD may include variousdesign features that facilitate dynamic implicit calibration. Severalexamples of such features are provided below.

As one example, the number of selectable UI elements presented on adisplay may be minimized, which reduces the possible number of UIelements on which the user's gaze is focused. For example, the number ofUI elements display may be restricted to be below a threshold determinedto enable accurate gaze location detection. As another example, thedistance between selectable UI elements may be maximized. As anotherexample, the similarity between the angle and length of vectors thatjoin pairs of selectable UI elements may be minimized. This featurewould allow the dynamic calibration process to have higher confidencewhen determining the UI element that gaze was shifted from and theelement that gaze was shifted to. As another example, the UI may beanimated such that resting gaze or selecting a UI element causesadditional UI elements to appear, creating a more predictable “flow ofgaze” and providing further opportunities for dynamic implicitcalibration.

The following discussion provides an example implementation of a dynamicimplicit calibration system, according to one implementation of thepresent disclosure. Additional examples are discussed below withreference to the figures.

In operation, the WHUD may identify when the user is looking at thedisplay via glint information received from a glint detection module,and may disable UI element selection and calibration when the user isnot looking at the display. The WHUD may obtain a calibration frominteraction start. The calibration may be used to infer gaze position inreal-time. This calibration may be updated at any time. The WHUD mayidentify when a UI element should be highlighted using a distancethreshold, for example. The WHUD may identify when the user is holdingtheir gaze on a point (A) on the display using time and distancethresholds, for example. If the point (A) is within the distancethreshold of a UI element (A′), the UI element (A′) may be highlighted,and the WHUD may infer that the user's gaze is resting on the UI element(A′).

In at least some implementations, the WHUD may also identify when theuser has shifted gaze to another point (B) on the display using time anddistance thresholds. The WHUD may compare the vector (A)→(B) to allvectors between pairs of UI elements (A′, B′), find the most similarvector (e.g., in terms of location, angle and magnitude), and compute aconfidence measure from the similarity measure. If the confidencemeasure is below a threshold, the WHUD may remove highlighting from UIelement (A′) and may not update the calibration. Otherwise, the WHUD mayinfer that the user's gaze is resting on UI element (B′), may highlight(B′), and may update the calibration. If the confidence of a series ofimplicit calibrations is sufficiently low, the WHUD may disable thehighlighting of UI elements to cue the user to shift gaze between UIelements to recalibrate, or perform the reset action. If the userselects a highlighted UI element, the WHUD may infer that the user'sgaze is resting on the UI element, and update the calibration. The WHUDmay improve performance by using a history of vectors, as opposed to themost recent vector, and may use efficient search algorithms to minimizecomputation.

As discussed elsewhere herein, aside from detecting abrupt changes,additional sensors (e.g., IMU, gyroscope, proximity sensor, stereocamera) may improve the calibration process by being used during anexplicit or implicit initial calibration, or an explicit or implicitdynamic calibration. During explicit initial calibration, input fromadditional sensors may be used to parameterize an algorithmic process ortrain a machine learning model that dynamically predicts coarsecalibration changes that are needed. This information may then be usedto assist the dynamic implicit calibration process, thereby improvingperformance.

In at least some implementations, an IMU may be used to detect abruptmovements that may necessitate the reset action. As another example, anIMU and a gyroscope sensor may be used to detect changes in user mode(e.g., different head orientations, talking, walking). As anotherexample, a proximity sensor and a front-facing touch sensor may be usedto detect the reset action discussed above. As another example, an IMU,gyroscope sensor and eye tracking may be used to detect the VestibuloOcular Reflex, which would indicate that the user is looking beyond theglasses display.

During use of a WHUD by a given user, there are many opportunities togather information that can be used to optimize the performance of theuser's WHUD. This information may also be used to augment a generalpopulation model of WHUD use, which may improve baseline eye trackingand calibration performance for new users. Many different approaches ofvarying complexity are possible.

For example, in at least some implementations, the results of eachuser's initial explicit calibration may be sent anonymously to a centralsystem (e.g., server) after requesting permission from the user. Theseresults may be used to augment a large library of labeled training casesthat is periodically used to train one or more new general explicitcalibration models.

As another example, a history of dynamic implicit calibration during UIuse, and conditions that required the reset action may be stored in anontransitory processor-readable storage medium of the WHUD. Thisinformation may be transmitted to a device (e.g., smartphone, laptop,wearable computer, cloud storage) periodically via a wired or wirelessinterface (e.g., USB®, Bluetooth®). In an explicit process similar todownloading and installing a software update, the user may trigger aprocess on the device that trains new prediction models and uploads themto the WHUD. These may be models that assist with eye tracking,calibration, detecting when the user is beginning to have trouble withthe UI, etc. The user may revert the update if the user detectssignificant performance degradation. Such information from this processmay be sent anonymously to a remote system for processing, afterrequesting permission from the user. This information may then be usedto improve the user experience, improve the default machine learningmodels that are included with WHUDs, improve companion software'sability to predict how helpful an update will be, etc.

FIG. 3 shows a method 300 of operation for a WHUD to perform explicitcalibration of an eye tracking process. As an example, the method 300may be implemented by the WHUDs 100 and 200 or FIGS. 1 and 2,respectively.

At 302, at least one processor of the WHUD may cause a pattern of UIelements (e.g., four UI elements, 8 UI elements) to be displayedsequentially on the display of the WHUD. As a non-limiting example, fourUI elements may be displayed sequentially, one UI element at a differentlocation (e.g., one UI element in each corner) of the display. At 304,for each UI element displayed and selected (e.g., clicked on) by theuser, the at least one processor may obtain a calibration point. Eachcalibration point may include a glint space point P_(g) in glint spaceobtained from a glint detection module (see FIG. 5) of the WHUD, and acorresponding display space point P_(d) in display space of the displaythat corresponds to the location (e.g., center location) of the UIelement displayed and selected by the user. A calibration pointrepresents an instant in time when a glint detection module produced aglint space point P_(g) while the user was gazing at a display spacepoint P_(d). As discussed below with reference to FIG. 5, the displayspace point may be received by the at least one processor from a UIlayer of the WHUD.

At 306, the at least one processor of the WHUD may fit a transform(e.g., affine transform) using the four calibration points bytranslating, rotating, shearing, and scaling the approximateparallelogram of the glint space to align with the rectangular shape ofthe display in display space.

At 308, the at least one processor may utilize the transform tosubsequently determine the gaze location of the user. For example, theat least one processor may subsequently receive a glint space point fromthe glint detection module, and may transform the glint space point to adisplay space point using the transform to determine the location on thedisplay of the WHUD where the user is currently gazing, which allows theuser to interact with the WHUD using his or her eye.

FIG. 4 shows a method 400 of operation for a WHUD to perform 1-pointre-centering of a calibration, such as the calibration discussed abovewith reference to the method 300 of FIG. 3.

At 402, the at least one processor of the WHUD may display a UI element,for example, during a calibration process or during regular use of theWHUD by the user.

At 404, the at least one processor may obtain a calibration pointcomprising a glint space point and a display space point representativeof the location of the UI element displayed on the display. The glintspace point may be received from the glint detection module and thedisplay space point may be received from the UI layer of the WHUD, forexample. In at least some implementations, the at least one processormay assume that the user is gazing at the center of the UI element whenthe UI element is selected (e.g., “clicked”) by the user.

At 406, the at least one processor may push the display space point ofthe calibration point backward through the transform (e.g., affinetransform) into glint space to obtain a transformed glint space point.The resulting transformed glint space point in glint space is likely notexactly equal to the glint space point from the calibration pointdetected by the glint detection module.

At 408, the at least one processor may determine a vector that separatesthe transformed glint space point with the glint space point of thecalibration point.

At 410, the at least one processor may adjust the translation of thetransform by the determined vector that separates the two glint spacepoints so that the transform maps the glint space point exactly to thedisplay space point, and hence perfectly fits the calibration point.

From a user experience (UX) perspective, it may be advantageous tominimize the frequency of explicit calibration and to support use-casesinvolving physical movement (e.g., walking, running, biking). Thus, inat least some implementations, a transform may be updated using a streamof incoming, arbitrary calibration points, to account for changes in therelationship between glint space and display spaces with respect torotation, shear, scale and translation due to movement of the supportframe of the WHUD. As discussed further below, such implementations maydetermine distinctions between selected calibration points, which mayhave higher confidence, may be older, and may be global, and inferredcalibration points, which may have lower confidence, may be newer, andmay be local. In at least some implementations, alternate transforms maybe maintained based on combinations of these calibration points.Further, inferred calibration points may be used to assist withcalibration recovery.

In at least some implementations, the at least one processor of the WHUDmay assign a probability to each UI element on a display based on thecurrent transform(s) and other information (e.g., UI layout, predictivemodels) as opposed to using the current transform to provide a gazelocation to the UI layer, which decides whether to highlight a UIelement based on a distance threshold.

As discussed above with reference to FIG. 2, in at least someimplementations a WHUD may be equipped with one or more auxiliarysensors, such as a proximity sensor, a gyroscope, an accelerometer, astereo camera, an inertial measurement unit (IMU), etc. Such auxiliarysensors may be used to assist with calibration of the eye trackingsystem of a WHUD.

FIG. 5 shows a block diagram of an eye tracking system 500 of a WHUDthat utilizes a machine learning model 502 of a dynamic calibrationsystem 512 to implement a dynamic calibration scheme. In at least someimplementations, one or more auxiliary sensors 504 may be used to trainthe machine learning model 502 that outputs optimized transformparameters 506 given current transform parameters 508 and currentauxiliary sensor values 510 from the one or more auxiliary sensors 504.The optimized transform parameters 506 may then be utilized by thedynamic calibration system 512 to improve gaze detection confidence. Asindicated by an output block 518, the dynamic calibration system 512 mayoutput the identity of a UI element, or may provide a “none” output ifgaze confidence is too low.

As discussed above, the dynamic calibration system 512 may interfacewith a glint detection module 514 and a UI layer 516 of the WHUD. Forexample, the glint detection module 514 may provide glint information(e.g., glint space points) to the dynamic calibration system 512. The UIlayer 516 may provide display information to the dynamic calibrationsystem 512, including the location of UI elements displayed on thedisplay of the WHUD. Such information may be used to perform thecalibration schemes discussed herein. Additionally, the dynamiccalibration system 512 may instruct the UI layer 516 to highlight a UIelement based on a determined gaze location.

In at least some implementations, this feature may utilize populationdata as well as particular user data collected during an initialexplicit or implicit calibration process. In at least someimplementations, the resulting machine learning models may be fine-tunedby user data collected during regular everyday use of the WHUD by theuser without the user being aware of such.

FIG. 6 shows an example method 600 of operation for a WHUD to perform aninitial explicit calibration of the eye tracking system of the WHUD thatgenerates an extensive amount of high quality training data in a veryshort time, and facilitates training of machine learning models (e.g.,machine learning model 502 of FIG. 5) to optimize affine transformparameters.

At 602, at least one processor of the WHUD may cause a UI element tomove in a determined pattern (e.g., rectangular pattern) on the displayof the WHUD (e.g., around a perimeter of the display). In at least someimplementations, the at least one processor may cause the UI element toreverse directions periodically.

At 604, the at least one processor may obtain a sequence of inferredcalibration points over a period of time (e.g., 30 seconds, 60 seconds)as the UI element moves on the display according to the determinedpattern. Each inferred calibration point may include a glint space pointcaptured by the glint detection module and a display space pointrepresentative of the location on the display at which the UI elementwas positioned when the glint space point was captured by the glintdetection module, which is also the location that the gaze of the useris inferred to be resting.

At 606, concurrent with obtaining each calibration point, the at leastone processor may record one or more auxiliary sensor values and atimestamp, and may logically associate such with theconcurrently-obtained calibration point in a nontransitoryprocessor-readable storage medium of the WHUD. Thus, the at least oneprocessor may store a plurality of calibration points which eachcomprise a display space point, a glint space point, and one or moreauxiliary sensor values. In at least some implementations, the at leastone processor may detect and remove outliers from the data.

At 608, the at least one processor may construct a first training casefrom the received data. A training case is a set of calibration pointsand their associated auxiliary sensor values. In at least someimplementations, the at least one processor may ensure that calibrationpoints that are maximally distant from each other are chosen for eachtraining case. FIG. 7 shows a non-limiting example of a plurality ofinferred calibration points (in display space) on a display 700 of theWHUD obtained during the acts 602-606 discussed above. In the example ofFIG. 7, the UI element moves in a counterclockwise direction in arectangular pattern. In other implementations, the UI element may movein various other patterns. Patterns that include corners (e.g., 90degree angle corners) may be advantageous because they cause the user'seye to change direction, which change can be readily detected by theglint detection module.

In this example, the at least one processor may select three inferredcalibration points A, B and C that lie closest to the bottom leftcorner, the bottom right corner, and the top right corner, respectively.In other implementations, more or fewer calibration points may beobtained at various locations on the display. Continuing with the aboveexample, the three inferred calibration points A, B and C were obtainedin fairly quick succession as the UI element moved along the display inthe counterclockwise rectangular pattern from point A to point B topoint C, etc. A number of additional points A+1, A+2, and A+3 may besequentially obtained between point A and point B, and a number ofadditional points B+1, B+2, and B+3 may be sequentially obtained betweenpoint B and point C. Further, a number of points C+1, C+2, etc., areobtained sequentially after point C was obtained.

The at least one processor may fit a transform (e.g., affine or othertransform) to points A, B and C and all points obtained between pointsA, B and C. If these points were captured during significant physicalmovement by the user of the WHUD, the points may contain a significantamount of noise. However, fitting the transform to a larger number ofpoints than the minimum points A, B and C reduces the impact of thisnoise, and produces a more accurate transform.

The at least one processor may obtain the auxiliary sensor values S_(C)recorded at the same time that point C was obtained. These auxiliarysensor values S_(C) capture the physical state of the WHUD at the end ofthe time window spanned by the calibration points used to generate theaffine transform.

The at least one processor may also obtain the auxiliary sensor valuesrecorded at the same time that points A, B, and C, and all pointstherebetween, were obtained. The at least one processor may compute theaverage of each auxiliary sensor value, S_(AVG), over these time points,which captures the average physical state of the WHUD over the timewindow spanned by the calibration points used to generate the affinetransform.

The at least one processor may then select the inferred calibrationpoint C+1 that was obtained immediately after the calibration point Cwas obtained, and obtain the auxiliary sensor values S_(C+1) recorded atthe same time that the point C+1 was obtained. These auxiliary sensorvalues S_(C+1) capture the current physical state of the WHUD, which isthe context for which the affine transform parameters of the currentaffine transform should be optimized.

The at least one processor may subtract S_(C) from S_(C+1) to obtainauxiliary sensor value deltas that represent how auxiliary sensor valueshave changed since the end of the time window spanned by the calibrationpoints used to generate the transform. These auxiliary sensor valuedeltas may be inputs to the machine learning model.

The at least one processor may also subtract S_(AVG) from S_(C+1) toobtain auxiliary sensor value deltas that represent how auxiliary sensorvalues have changed with respect to the average auxiliary sensor valuesduring the time window spanned by the calibration points used togenerate the transform. These auxiliary sensor value deltas may also beinputs to the machine learning model.

The glint space and display space points from the point C+1 may then beused to evaluate the machine learning model performance on the generatedtraining case.

At 610, the at least one processor may utilize the inputs from thetraining case to train a machine learning model to output six optimizedaffine transform parameters.

In at least some implementations, the at least one processor may ensurethat the points A, B and C have “traveled around the display,” and areroughly evenly distributed. In at least some implementations, the atleast one processor may ensure that roughly half of the training datawas captured in a counterclockwise scenario, as shown in FIGS. 7 and 8,and the other half was captured in a clockwise scenario. Capturing alarge volume of symmetrical, granular training data in this fashion mayhelp the machine learning model learn to compensate for significanthigh-frequency physical movement.

In at least some implementations, the best of these training cases maybe contributed to a large database of training data obtained from theinitial calibration of many users. Further, a machine learning model maybe trained on each of these sets of training cases (population anduser). Additionally, a weighted mixture of these two models may bedetermined that optimizes performance on the user's training data.

In at least some implementations, the at least one processor mayinstruct the user to repeat the initial explicit calibration process indifferent determined user modes (e.g., slowly moving user's head,talking, walking, running). For each user mode, the at least oneprocessor may obtain a new set of training cases, contribute to apopulation model, train machine learning models, and optimize a mixturemodel, as discussed above. Using training cases from all user modes, theat least one processor may train and optimize a new mixture model thatpredicts the current user mode from auxiliary sensor values. This modelcan then select the current user-mode-specific mixture model (or aweighted combination thereof) to optimize transform parameters.

In at least some implementations, during everyday use of the WHUD, theat least one processor of the WHUD may store selected calibration pointsin the same way that inferred calibration points are stored duringinitial explicit calibration. The at least one processor may use thesecalibration points to augment population models, optimize mixture modelparameters, and to periodically retrain user models.

FIG. 9 shows a method 900 of operation for a WHUD to provide dynamiccalibration of an eye tracking system of the WHUD. As an example, themethod 900 may be implemented by the WHUDs 100 and 200 of FIGS. 1 and 2,respectively.

At 902, at least one processor of the WHUD may obtain a calibrationpoint model M₁ (<P_(g), P_(d)>₁, . . . , <P_(g), P_(d)>_(N)), whereP_(g) is a point in glint space, P_(d) is a point in display space, andN is the number of calibration points in the model M₁. Each calibrationpoint may include a glint space point in a glint space and a displayspace point in a display space of a display of the WHUD, as discussedabove. The glint space point may be captured by a glint detection moduleof the WHUD, and may be representative of a position of an eye of a userof the WHUD, as discussed above. The display space point may berepresentative of a location on the display at which a gaze of the useris inferred to be resting when the glint space point is captured by theglint detection module. As discussed above, in implementations whereinauxiliary sensors are utilized, at least some of the calibration pointsmay also include auxiliary sensor values.

In at least some implementations, the at least one processor may obtaina calibration point model by causing the display to generate aninteraction start screen that supports explicit and/or implicitcalibration. When the interaction start screen is enabled, the screenwill populate a calibration point model M₁ with high quality calibrationpoints which provides a best-case starting point for dynamiccalibration.

In at least some example implementations, the interaction start screenmay contain five selectable UI elements (e.g., round UI elements),located at the four corners and the center of the display. In anexplicit calibration mode, the at least one processor may present thecorner UI elements sequentially one at a time, and may require the userto gaze and select (e.g., click on) each one of the UI elements. Each UIelement may change color briefly when a selection occurs, even thoughthe UI element was not highlighted. The dynamic calibration systemachieves high gaze confidence from these four selected calibrationpoints, and will then exit a calibration recovery mode, which enables UIelement highlighting. The at least one processor may then cause thecenter UI element to be displayed on the display. Once the user gazes onthe center UI element and the center UI element is highlighted,selecting the center UI element allows the user to continue with theinteraction.

In an implicit calibration mode, the at least one processor may presentall UI elements (e.g., five UI elements) simultaneously on the display.The user may be allowed to shift gaze between the UI elements until thedynamic calibration system achieves adequate gaze confidence and exitscalibration recovery mode, thereby enabling UI element highlighting.Once the user gazes on the center UI element and the center UI elementis highlighted, selecting the center UI element allows the user tocontinue with the interaction.

In at least some implementations, the at least one processor may obtaina base calibration model that most closely corresponds to currentauxiliary sensor values. As discussed above with reference to FIG. 6,such may be obtained from a stored library, for example.

In at least some implementations, the at least one processor may obtaina plurality of calibration point models at a given time. For example,the at least one processor may utilizes a mixture of models to make aprediction that will be more reliable than the prediction of any onemodel. More specifically, between selections, the at least one processorhas to infer the sequence of UI elements that the user's gaze traversed,and each model represents a different inference.

Thus, after the act 902, the system has obtained a one or morecalibration point models.

At 904, the at least one processor may generate a transform from theglint space to the display space for each of the one or more modelsusing any suitable transform method. In at least some implementations,an affine transform T₁ is used to generate the transform from the glintspace to the display space for each of the models.

The following discussion provides a non-limiting example algorithm forcomputing an affine transform T_(i) from a calibration point modelM_(i). A variable numCalibrationPoints may be defined as the length ofthe model M_(i). A (numCalibrationPoints*2×6) matrix A may beconstructed, where:

A _(j)=[x _(gj) y _(gj)0 0 1 0]

A _(j+1)=[0 0x _(gj) y _(gj)0 1]

A (numCalibrationPoints*2×1) vector b may be constructed, where:

b _(j) =x _(dj)

b _(j+1) =y _(dj)

The at least one processor may solve (e.g., least squares) for the 6×1vector p, the parameters of the affine transform T_(i), using the QRdecomposition (QRD) or singular value decomposition (SVD) method. LetT_(i)(x_(g), y_(g))=(p₁*x_(g)+p₂*y_(g)+p₅, p₃*x_(g)+p₄*y_(g)+p₆)=(x_(d),y_(d)).

The QRD method may be used to determine the parameters of the affinetransform, as the QRD method may offer the best time complexity and goodnumerical stability. For applications that may require more advancedfunctionality, such as the ability to weight calibration points and/orto ignore outliers, the SVD method may be used.

Thus, after act 904, the system has fit T₁ to M₁. As discussed above, T₁is the affine transform from glint space to display space thathypothesis M₁ predicts is the current transform.

At 905, if the at least one processor may detect whether the user's gazeis resting (e.g., for a period of time).

If the at least one processor detects that the user's gaze is resting,at 906 the at least one processor may obtain glint information from theglint detection module and create new models as needed. Morespecifically, the at least one processor may branch each existing modelby appending an inferred calibration point for each UI element displayedon the display to create (numModels*numUIElements) new child model(s),which replace the previous parent model(s). For example, if the systemhas three parent models and the display has 10 UI elements displayed,the at least one processor generates 30 child models, 10 child modelsfor each of the three parent models. Each of the child models includes anew calibration point appended to the child model's parent model. Thenew calibration point includes a glint space point obtained from theglint detection module and a display space point that corresponds to theone of the UI elements with which the child model is associated. Thus,each of the new calibration points have the same glint space points anddifferent display space points.

At 908, the at least one processor may utilize the models to determinethe probability that the user's gaze is resting on each UI element.Specifically, the at least one processor may compute the totalprobability of the user's gaze resting on each UI element, a function ofmodel probabilities, to create a probability distribution over the UIelements displayed on the display.

At 910, the at least one processor may determine the one UI elementdisplayed on the display that the user's gaze is most likely resting on,and may determine a “confidence value” that the user's gaze is restingon that UI element. Specifically, the at least one processor may utilizethe determined probability distribution to determine the UI element withthe most probability. The features of the probability distribution as awhole may be used to define the confidence value. If gaze confidence ishigh enough (e.g., above a threshold), the UI may highlight that UIelement, which indicates to the user that the system has detected thatthe user's gaze is focused on that UI element.

Returning back to act 905, if the at least one processor does not detectthat the user's gaze is rested, at 907 the at least one processor maydetect whether a highlighted UI element has been selected (e.g., clickedon).

If a highlighted element has been selected, at 912 the at least oneprocessor may obtain glint information from the glint detection module,and may construct a selected calibration point <P_(g), P_(d)> thatincludes a glint space point from the glint detection module and adisplay space point that corresponds to the location on the display ofthe selected UI element. The display space point P_(d) may be inferredto lie at the center point of the highlighted UI element. T_(i)(x_(g),y_(g)) may not necessarily equal the display space point P_(d), due totransform error, movement of the support frame since T_(i) was computed,and/or other error. However, T_(i)(x_(g), y_(g)) should be close to thedisplay space point P_(d) for the most probable models M_(i).

In at least some implementations, selections are ignored if a UI elementis not highlighted. In at least some implementations, the obtainedselected calibration point may be used to discard models that did notpredict that the user's gaze is resting on the current UI element. Inother words, the selected calibration point may be used as the“confirmation” that can be used to assess past inferences.

To minimize the impact of accidental gaze shift before a selection, if aselection occurs while gaze is not being held, a short time after gazewas resting on a selectable UI element (e.g., determined by a timethreshold), the at least one processor may assume that the user shiftedgaze just prior to the selection. The at least one processor mayassociate the selection with the previous UI element, register theselection as usual, and obtain a selected calibration point. If thereset action occurs a short time after obtaining a selected calibrationpoint (e.g., determined by a time threshold), the user may have selecteda highlighted UI element while not gazing at it. In such instances, theat least one processor may remove the selected calibration point priorto proceeding with calibration at interaction start. If a “dubious”selected calibration point is obtained (e.g., determined via analgorithm and/or a separate ML module), the system may choose to ignoreit. This feature minimizes the impact of unintended selections, andreduces the need for the reset action as described above.

At 914, the at least one processor may discard one or more unlikelymodels. In at least some instances, it may be too computationallyexpensive to retain all models. In a “resting gaze” case, the at leastone processor may use various approaches to determine which models todiscard. For example, the at least one processor may keep the mostprobable N number of models, may keep those models above a probabilitythreshold, etc. In a “clicked” case, the at least one processor mayretain a single model that becomes model M₁, the most probable modelthat ends with a calibration point corresponding to the UI element thatwas clicked. As another example, the at least one processor may retain asmall number of models that end with this calibration point. As anotherexample, the at least one processor may replace the last inferredcalibration point in these models with the obtained selected calibrationpoint, or an average of the two. After discarding one or more models,the at least one processor may normalize model probabilities such thatthe probability of the remaining, non-discarded models, sums to 1.

At 916, the at least one processor may optimize the one or more modelsby evicting zero or more calibration points. After the one or moremodels have been optimized, control may return to act 904, and theprocess may iterate continuously until the end of the interaction.

The following discussion provides a number of different strategies thatmay be used to evict one or more calibration points from a calibrationpoint model.

A positive integer N may be defined as the number of calibration pointsin a calibration point model M_(i). Then N must be bounded. N_(Min) andN_(Max) may be defined as the lower and upper bounds, respectively, ofN. Therefore, when a new calibration point is added to M_(i), it may benecessary to evict calibration point(s) from M_(i) to ensure that N doesnot exceed N_(Max).

Within these bounds, N may be chosen to optimize dynamic calibrationperformance for the current user in the current context. ChoosingN=N_(Min) causes T_(i) to fit the most recent N_(Min) calibration pointsthe best. But it may be desirable for T_(i) to fit the most recentN_(Min)<=K<=N_(Max) calibration points, such that the amount of movementof the support frame that occurred during capture of the calibrationpoints is minimal, and the distribution of the points in glint space ismaximal. The former may be more important than the latter. It is notedthat evicting calibration point(s) from one or more models may causemultiple, previously unique models to contain the same sequence ofcalibration points. If this occurs, duplicate models may be discarded.

N_(Min) is defined by the transform. For example, N_(Min) is equal tothree for an affine transform, and is significantly more for a nonlineartransform. If N is smaller than N_(Min), T_(i) cannot be computed. Ifthis is the case, the system may be in calibration recovery mode,discussed below.

N_(Max) is defined by the hardware of the WHUD, and the time complexityof the dynamic calibration algorithm. If N becomes too large, the systemwill not be able to keep up with gaze input from the glint detectionmodule. In at least some implementations, a value for N_(Max) (e.g., 5,7, 10) may be determined via experimentation. The value for N_(Max) maybe selected to ensure that this value is safely smaller than a valuethat would begin to cause a slowdown. In at least some implementations,a developer configuration option may be provided to allow adjustment ofN_(Max). Any manipulation of a model M_(i) must ensure that N adheres tothese bounds.

Many approaches to evicting calibration points from M_(i) are possible.Any such approaches may rely on an assumption that gazeable/selectableUI elements are reasonably distributed throughout the display, andsalience is reasonably distributed throughout the display. Suchapproaches may also rely on an assumption that movement of the supportframe of the WHUD affects calibration accuracy more than transformerror.

One approach evicts the oldest point(s) from M_(i) to ensure that N isless than or equal to N_(Max). Such an approach is simple,computationally cheap, and works well in practice. The oldest point(s)are evicted without considering the distribution of calibration pointsin M_(i). Further, since M_(i) spans a time window where no calibrationpoints have been removed, the distribution of calibration points inM_(i) is expected to be fairly even, without any explicit interventionfrom the algorithm. Further, N can be adjusted to manipulate the size ofthis time window, allowing transform fit to vary smoothly betweentemporally recent, spatially local and temporally historical, spatiallyglobal. In at least some implementations, N may be adjusted dynamically(e.g., via algorithm and/or a separate ML module) to optimize M_(i) andT_(i) for the current context and user mode.

For some applications, a more complicated eviction strategy than thatdescribed above may be desirable to achieve good dynamic calibrationperformance in user modes involving physical movement.

Strategies that evict solely to optimize point distribution may not besuccessful, and may suffer from several issues. Generally, evicting tooptimize point distribution should have lower priority than evicting tominimize the amount of physical movement that occurred during capture ofthe points in M_(i), since it is assumed that movement of the supportframe of the WHUD affects calibration accuracy more than transformerror. Further, evicting to optimize point distribution may not benecessary, since gazeable/selectable UI elements may be reasonablydistributed throughout the display. Further, old calibration pointscould heavily skew T_(i). Additionally, gaze sequences may be local,causing recent valuable calibration points to be evicted. The system mayremove the most recent K calibration points from eligibility foreviction to help mitigate this issue, but such may require selection ofK to weight priority between point recency and point distribution.

In at least some implementations, an eviction approach is used thatoptimizes T_(i)'s fit to the current selected point, or a subset ofM_(i) including the current selected point.

According to another eviction approach, the display may be dividedevenly (e.g., divided into a grid). The system may maintain onecalibration point per region, evict existing calibration points in aregion, and refit T_(i) with the new calibration point.

According to another eviction approach, the at least one processor mayevict a nearest neighbor of the newly obtained calibration point ifcloser than a distance threshold, and then refit T_(i).

According to another eviction approach, the at least one processor mayplace importance weights on having recent points versus having an evenpoint distribution. The at least one processor may compute animprovement value that results from evicting each calibration pointgiven its age and location, and evict the calibration point thatrealizes the highest improvement value. The at least one processor maythen refit T_(i).

According to another eviction approach, the at least one processor mayevict a nearest neighbor if it is closer than a distance threshold, andsubsequent neighbors, in order from closest to farthest, if doing socontinues to improve T_(i)'s fit to the current selected calibrationpoint. The at least one processor may then refit T_(i). This approacheliminates calibration points that are close to and inconsistent withthe current selected calibration point (i.e., not recent). However, itmay be desirable to eliminate calibration points that are distant andnot recent as well.

According to another eviction approach, the at least one processor mayrefit T_(i), evict a calibration point that T_(i) fits the worst, andthen refit T_(i). After a significant movement of the support frame ofthe WHUD, the calibration point that T_(i) fits the worst may be themost recent calibration point. This may be prevented programmatically.When a second calibration point is subsequently obtained, the firstcalibration point would be the second-most-recent calibration point,which would be more accurate than any of the other calibration pointssets except for the current (second) calibration point. In at least someimplementations, the system may detect when a significant movement ofthe support frame has occurred, and switch to evicting the oldestcalibration point(s) temporarily.

According to another eviction approach, the at least one processor mayrefit T_(i), evict point(s) that T_(i) fits worse than a threshold, andthen refit T_(i).

According to another eviction approach, the at least one processor maystart with the N_(Min) most recent points, excluding the currentselected point, grow a calibration point toward the end of M_(i), andfit a new transform to the points in the set each time. The at least oneprocessor may evaluate how well each of these transforms fits thecurrent selected point. The at least one processor may select the besttransform, let M_(i) consist of the points that this transform wasfitted to, and then refit T_(i). The transform that fits the currentselected point the best will likely be the one fitted to the N_(Min)most recent points, excluding the current selected point.

Another eviction approach is similar to the one discussed immediatelyabove, but the at least one processor may evaluate fit against thecurrent selected point and all of the points in the set. This approachmay produce a transform that generalizes better than above.

Another eviction approach is similar to the two approaches discussedimmediately above, except the at least one processor may grow the setexcluding the first K points in addition to the current selected point.The at least one processor may evaluate how well each of the transformsfits the excluded points and all of the points in the set.

According to another approach, at least one proximity sensor value fromat least one proximity sensor may be used to determine whether the newpoint is close enough to the previous point(s). In at least someimplementations, the at least one processor may store all the pointsaccording to what the proximity sensor value(s) was/were for thosepoints, then recall the set of stored points which correspond to the newpoint's proximity sensor bucket(s).

Generally, a hybrid eviction approach that prioritizes maintainingrecent points and a good point distribution, while using an estimationmethod to predict eviction impact (to minimize reliance on transformfitting) may be effective. Further, an eviction approach that minimizesthe assumption that gazeable/selectable UI elements are reasonablydistributed throughout the display may be beneficial. Such may beachieved if the eviction strategy maintains adequate gaze confidence ina scenario where the user shifts gaze from UI element A to distant UIelement B, then to nearby UI element C, then back and forth between UIelement B and UI element C many times, then back to UI element A.

Due to continuous movement of the support frame with respect to the eye,user gaze location can only be known for certain at the instant the userselects (e.g., clicks on) a highlighted UI element. At all other times,gaze location must be inferred. As discussed above with reference to act910 of FIG. 9, the system may assign a gaze confidence to a gazeinference to determine whether UI element highlighting should be enabledor disabled. To be effective, this confidence value may depend on the UIneighborhood surrounding the inferred element, and hence theprobabilities of alternate inferences. For example, if the UIneighborhood surrounding the inferred UI element is sparse, theconfidence value may be higher. Conversely, if the UI neighborhood isdense, the confidence value may be lower. Therefore, a probabilitydistribution may be defined, and the maximum likelihood inference and agaze confidence may be determined via this probability distribution.This probabilistic approach allows a variable scope and depth of contextto be considered, but defines probabilities relative to the entirecontext. A simple (e.g., geometric distance-based) or arbitrarilycomplex (e.g., input from predictive models and auxiliary sensors)probability distribution may be implemented.

A probability distribution GP may be defined over all currentlygazeable/selectable UI elements E that defines the probability that theuser is currently gazing at each UI element E_(k).

The following discussion provides several different approaches forextending calibration point models. As discussed above, if a selectedcalibration point was just obtained, the only model that exists is M₁,which ends with the most recent selected calibration point. Otherwise, anumber of models M_(i) may exist, each containing a sequence ofcalibration points. Some models may begin with a common sub-sequence ofcalibration points (e.g., descendants of a common ancestor or parentmodel). However, no two models contain the same sequence of calibrationpoints (e.g., each model makes at least one unique inference).

Allow numElements to be the number of currently gazeable/selectable UIelements E_(k). As discussed above, for each parent model M_(i), the atleast one processor may create numElements child models that extendM_(i), where an inferred calibration point <P_(g), P_(d)> is appended tothe jth child model, such that P_(d) is the center point of UI elementE_(j). Such branches and extends the hypothesis represented by M_(i) tocover all possible current gaze scenarios. The system then has manychild models. The at least one processor may move the parent models intoa separate set P, let the child models constitute M, and ensure thateach child model references its parent model. A probability may beassigned to each child model. Intuitively, a child model's probabilitymay be a function of its parent model's probability, among othervariables.

The following discussion provides example implementations of definingmodel probabilities. Per Bayes' Rule:

P(Model|Data)=(P(Model)*P(Data|Model))/P(Data)

A model M_(i) includes M_(i).P_(g) and M_(i).Element, which correspondto the most recent inferred calibration point. The data, which comprisesa sequence of calibration points, the last K of which have beeninferred, are M_(i)'s parent model's data, its sequence of calibrationpoints and its transform, which has been fitted to this sequence.

Then the probability of a model M_(i) given the data can be defined asfollows:

P(M _(i) |M _(i).Parent.Data)=(P(M _(i).Parent)*P(M _(i).Parent.Data|M_(i)))/Σ_(j)(P(M _(j).Parent)*P(M _(j).Parent.Data|M _(j)))

The prior probability of a model M_(i) is the probability of its parentmodel. The likelihood of the data given a model M_(i) can be defined asfollows:

P(M _(i).Parent.Data|M _(i))=f(M _(i).Parent.Data,M _(i) .P _(g) ,M_(i).Element)*g(M _(i).Parent.Data,M _(i).Element)

The at least one processor may define a function f(M_(i).Parent.Data,P_(g), Element) with range [0, 1] as follows:

f(M _(i).Parent.Data,P_(g),Element)=1−((Element.Centerpoint−ensureOnDisplay(M_(i).Parent.Data.Transform(P _(g))))²/Element.MaxDistToCornerSquared)

The above approach uses M_(i)'s parent's transform to map P_(g) intodisplay space to produce P_(d)′. If P_(d)′ lies outside of the display,the method ensureOnDisplay(P_(d)′) constructs a line between P_(d)′ andthe center of the display, finds the intersection of this line and theclosest display edge, and adjusts P_(d)′ to lie at this intersection.This approach computes the squared distance error between the centerpoint of Element (P_(d)) and P_(d)′. Another approach may compute all ofthe T_(i)(P_(g)) first, find the maximum squared distance error, and usethis as the maximum for the current resting gaze when computing allmodel probabilities.

The above approach takes this as a fraction of the maximum possiblesquared distance error (i.e., the distance between the center point ofElement and the farthest display corner squared, a constant value). Ifthis fraction approaches 1, error approaches the maximum. If thisfraction approaches 0, error approaches the minimum. Therefore, thisfraction is subtracted from 1 to produce a probability value on [0, 1].This is the probability that the user is gazing at UI element Element,assuming that all inferred calibration points in M_(i) are correct.

As discussed above, the at least one processor may leverage input from aseparate machine learning module that has learned (e.g., via populationand/or user data) to optimize transform parameters given auxiliarysensor input, to optimize M_(i).Parent.Data.Transform for the currentcontext and user mode prior to applying it to P_(g). Increasing theaccuracy of the transform may allow a more meaningful probability valueto be obtained, which reduces the entropy of GP and increase gazeconfidence.

The function g(M_(i).Parent.Data, Element) with range [0, 1] may bedefined as follows:

g(M _(i).Parent.Data,Element)=1

In other implementations, the at least one processor may obtain aprobability value on [0, 1] from a separate machine learning module thathas learned (e.g., via population and/or user data) to predict UI usagepatterns (e.g., element gaze and selection counts, typical gaze andselection flows) for a given UI layout in different contexts and usermodes. Such module may compute and return:

P(Element|M _(i).Parent.Data.ElementSequence).

Penalizing unlikely element sequences (models) may reduce the entropy ofGP and increase gaze confidence, as discussed below.

In at least some implementations, the at least one processor mayimplement a unigram model that computes the fraction of times that eachUI element has been gazed for a given UI layout: P(Element). In otherimplementations, the at least one processor may implement an N-grammodel that computes the fraction of times that each UI element has beengazed/selected after a given short sequence of gaze/selection events fora given UI layout: P(Element| The most recent N−1 elements fromM_(i).Parent.Data.ElementSequence).

In at least some implementations, the at least one processor mayimplement a Hidden Markov Model, recurrent neural networks (RNNs), or atemporal model that computes the probability that each UI element willbe gazed/selected after a given longer sequence of gaze/selection eventsfor a given UI layout:

˜P(Element|M _(i).Parent.Data.ElementSequence)

The probability of the data (the denominator) is simply the totalprobability assigned to all models. This denominator ensures thatΣ_(i)P(M_(i)|M_(i).Parent.Data)=1.

The following discussion provides example implementations for combiningmodel probabilities to define GP. Let GP be a discrete probabilitydistribution over E, such thatGP(E_(k))=Σ_(j)P(M_(i)|M_(j).Parent.Data), where M_(j).Element=E_(k). Itis not necessary to normalize this distribution, as the totalprobability assigned to all models already equals 1. Hence, models witha higher probability provide a stronger “vote” for the UI element thatthey infer gaze to be resting on. Using GP, the at least one processormay determine the most probable UI element, and may determine whetherthe gaze confidence threshold is met, as discussed further below.

If the gaze confidence threshold is not met, or if gaze confidence hasbeen trending downward, optimization of models may be performed toreduce the entropy of GP and increase gaze confidence. Afteroptimization is performed, model probabilities and GP must berecomputed. Since it cannot be guaranteed that optimization will improveGP, the original models and GP should be retained until they areimproved upon. According to one approach, the at least one processor maydiscard improbable models to reduce the time complexity of subsequentoptimizations, and reduce noise prior to computing GP. According toanother approach, the at least one processor may evict inaccuratecalibration points from parent models. The at least one processor maythen refit their transforms to achieve better transform accuracy andmore meaningful probability values.

The following discussion provides example implementations for retainingthe most probable models. Just as the number of calibration points in amodel must be bounded (N_(Max)), the number of models must also bebounded (M_(Max)), to allow the system to keep up with gaze input fromthe glint detection module. Suitable values for M_(Max) (e.g., 5, 7, 10)may be determined via experimentation, for example. The system mayensure that this value is safely smaller than a value that would beginto cause a slowdown. Additionally, in at least some implementations adeveloper configuration option may be provided to allow selectiveadjustment of M_(Max).

In operation, the at least one processor may sort the models indescending order of probability, and retain the M_(Max) models with thehighest probability. It is possible that fewer than M_(Max) models havesignificant probability. In this case, fewer than M_(Max) models may beretained. For example, a probability threshold may be defined, wheremodels with probability lower than this threshold (e.g., 0.1) are notretained.

After the most probable models M₁, . . . , M_(K) have been retained, theat least one processor may evict calibration point(s) from each modelM_(i) if necessary to accommodate the most recent calibration point,and/or to optimize the model. The at least one processor may then refitT_(i) to M_(i) for each model, and renormalize model probabilities toaccount for the probability that was lost when models were discarded,according to the following equation for all models:

P(M _(i) |M _(i).Parent.Data)=(P(M _(i) |M _(i).Parent.Data)/Σ_(j) P(M_(j) |M _(j).Parent.Data))

The at least one processor may then store each model's probabilityvalue, which becomes the prior probability of each of the model's childmodels.

The following discussion provides example implementations for gazeconfidence and UI element highlighting. When gaze is resting, GP may becomputed as described above. It is straightforward to find the E_(k)with the highest probability, and compare this probability to theprobabilities of the other UI elements. E₁ may be defined as thehighest-probability UI element and E₂ be the second-highest-probabilityUI element as defined by GP. The gaze confidence threshold is met whenthe probability that gaze is resting on E₁ is greater than or equal tothe gaze confidence threshold times the probability that gaze is restingon E_(2.) In at least some implementations, the gaze confidencethreshold may have a default value of 2.0 (i.e., gazeconfidence=2*100/(numElements+1)), for example, and may be selectivelyadjustable.

This simple approach was chosen over more complex methods because evenwith worst-case threshold clearance and entropy, probabilitydistributions are intuitively acceptable from a confidence perspective,as long as the number of UI elements is reasonably small. The list belowshows exemplary gaze confidence thresholds on a 100% scale for displayshaving between 2 and 10 UI elements displayed thereon.

-   -   2 UI elements; gaze confidence threshold is 66;    -   3 UI elements; gaze confidence threshold is 50;    -   4 UI elements; gaze confidence threshold is 40;    -   5 UI elements; gaze confidence threshold is 33.3;    -   6 UI elements; gaze confidence threshold is 29;    -   7 UI elements; gaze confidence threshold is 25;    -   8 UI elements; gaze confidence threshold is 22;    -   9 UI elements; gaze confidence threshold is 20; and    -   10 UI elements; gaze confidence threshold is 18.

If the gaze confidence threshold is met, the at least one processor mayassume that the user is gazing at the highest-probability UI element. Ifthe WHUD is currently in calibration recovery mode, the at least oneprocessor may exit this mode, which enables UI element highlighting. Ifthe UI element is selectable, the at least one processor may cause theUI element to be highlighted. Otherwise, if not in calibration recoverymode, the processor may enter calibration recovery mode, which disablesUI element highlighting.

The following discussion provides example implementations for acalibration recovery mode and a reading mode for a WHUD.

When gaze confidence is below the gaze confidence threshold (and theuser makes a selection), the dynamic calibration system may entercalibration recovery mode. In this mode, UI element highlighting isdisabled until the system exits this mode, which cues the user to shiftgaze between UI elements to facilitate calibration recovery. In thismode, the system's goal is to reach the gaze confidence threshold asquickly as possible. Models are extended and evaluated as discussedabove, and although the probabilities of these models will be lowinitially, the probabilities of some models will quickly climb as gazeis shifted between UI elements. Since the user is expected to shift gazewhen the dynamic calibration system is in this mode, the user's gazesequence may be unpredictable and atypical. Therefore,g(M_(i).Parent.Data, M_(i).Element) should unconditionally return 1 whenthe system is in this mode, or model probabilities may become too small,preventing the gaze confidence threshold from being met.

In at least some implementations, the at least one processor may reduceN to N_(Min) (e.g., 3 for affine transform) during calibration recoverymode. This will eliminate the oldest calibration points from all modelsand immediately update model transforms, and will allow each incomingcalibration point to have a more immediate and significant impact on itsmodel's transform.

The at least one processor may also loosen model retention behavior. Forexample, the at least one processor may disable the probabilitythreshold that models usually must exceed to be retained, ensuring thatM_(Max) models are retained each time gaze is resting. The at least oneprocessor may also allow additional (or all) models to be retained(exceeding M_(Max)) every other time gaze is resting. When gaze restsagain, the at least one processor may prune the models as usual.

When gaze is resting, at least one model will generally make the correctinference, but it is not guaranteed that this model will havesignificant probability relative to the other models. This is becausethis model's existing calibration points and transform may no longer beaccurate due to movement of the support frame. However, retaining thismodel allows the model's correctly inferred calibration point to improvethe accuracy of its transform. Since one of this model's child models isguaranteed to make the correct inference the next time gaze is resting,this child model may be assigned a higher probability due to theimproved accuracy of its parent model's transform, and hence has ahigher probability of being retained. If this occurred, convergence maybe likely, as this child model's transform would be fitted to tworecent, correct calibration points, and a third calibration point ofunknown accuracy, and one of this model's children is guaranteed tocorrectly infer the next calibration point.

Therefore, retaining additional models increases the probability thatthe model(s) that have made the correct inference are retained, whichincreases the probability of calibration recovery. Retaining asignificant number of models (or all models) every other time gaze isrested may greatly speed the convergence of calibration recovery.However, exceeding M_(Max) by definition may prevent the system frombeing able to keep up with input from the glint detection module. But,this may be acceptable in calibration recovery mode, and input from theglint detection module can be handled as needed. In at least someimplementations, the at least one processor may perform advancedoptimization and selection of models to speed the convergence ofcalibration recovery

In at least some implementations, a reading mode may be implemented thatobtains inferred calibration points when the system detects that theuser is reading. As an example, the system may detect that the user isreading by detecting that the user is gazing at a UI element that is aknown text container. As another example, the system may detect that theuser is reading by detecting that the user's gaze is moving in saccades.As another example, the system may detect that the user is reading basedon the text content where the saccades should land (in general or forthe user), and obtain inferred calibration points. As another example,the system may determine the line of text that is currently being read,and the current reading speed of the user. Such functionality may alsobe used to support an auto-scrolling feature for sizeable bodies oftext.

The various implementations described herein may employ ensemble machinelearning methods that make use of and combine different models andmachine learning techniques.

The foregoing detailed description has set forth various implementationsof the devices and/or processes via the use of block diagrams,schematics, and examples. Insofar as such block diagrams, schematics,and examples contain one or more functions and/or operations, it will beunderstood by those skilled in the art that each function and/oroperation within such block diagrams, flowcharts, or examples can beimplemented, individually and/or collectively, by a wide range ofhardware, software, firmware, or virtually any combination thereof. Inone implementation, the present subject matter may be implemented viaApplication Specific Integrated Circuits (ASICs). However, those skilledin the art will recognize that the implementations disclosed herein, inwhole or in part, can be equivalently implemented in standard integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more controllers(e.g., microcontrollers), as one or more programs running on one or moreprocessors (e.g., microprocessors), as firmware, or as virtually anycombination thereof, and that designing the circuitry and/or writing thecode for the software and or firmware would be well within the skill ofone of ordinary skill in the art in light of this disclosure.

Those of skill in the art will recognize that many of the methods oralgorithms set out herein may employ additional acts, may omit someacts, and/or may execute acts in a different order than specified.

In addition, those skilled in the art will appreciate that themechanisms taught herein are capable of being distributed as a programproduct in a variety of forms, and that an illustrative implementationapplies equally regardless of the particular type of signal bearingmedia used to actually carry out the distribution. Examples of signalbearing media include, but are not limited to, the following: recordabletype media such as floppy disks, hard disk drives, CD ROMs, digitaltape, and computer memory.

The various implementations described above can be combined to providefurther implementations. To the extent that they are not inconsistentwith the specific teachings and definitions herein, all of the U.S.patents, U.S. patent application publications, U.S. patent applications,foreign patents, foreign patent applications and non-patent publicationsreferred to in this specification and/or listed in the Application DataSheet which are owned by Thalmic Labs Inc., including but not limitedto: US Patent Application Publication No. US 2015-0378161 A1, US PatentApplication Publication No. 2016-0377866 A1 U.S. Non-Provisional patentapplication Ser. No. 15/046,234, U.S. Non-Provisional patent applicationSer. No. 15/046,254, US Patent Application Publication No. US2016-0238845 A1, U.S. Non-Provisional patent application Ser. No.15/145,576, U.S. Non-Provisional patent application Ser. No. 15/145,609,U.S. Non-Provisional patent application Ser. No. 15/147,638, U.S.Non-Provisional patent application Ser. No. 15/145,583, U.S.Non-Provisional patent application Ser. No. 15/256,148, U.S.Non-Provisional patent application Ser. No. 15/167,458, U.S.Non-Provisional patent application Ser. No. 15/167,472, U.S.Non-Provisional patent application Ser. No. 15/167,484, U.S. ProvisionalPatent Application Ser. No. 62/271,135, U.S. Non-Provisional patentapplication Ser. No. 15/331,204, US Patent Application Publication No.US 2014-0198034 A1, US Patent Application Publication No. US2014-0198035 A1, U.S. Non-Provisional patent application Ser. No.15/282,535, U.S. Provisional Patent Application Ser. No. 62/268,892,U.S. Provisional Patent Application Ser. No. 62/322,128, U.S.Provisional Patent Application Ser. No. 62/428,320, and U.S. ProvisionalPatent Application Ser. No. 62/533,463, are incorporated herein byreference, in their entirety. Aspects of the implementations can bemodified, if necessary, to employ systems, circuits and concepts of thevarious patents, applications and publications to provide yet furtherimplementations.

These and other changes can be made to the implementations in light ofthe above-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificimplementations disclosed in the specification and the claims, butshould be construed to include all possible implementations along withthe full scope of equivalents to which such claims are entitled.Accordingly, the claims are not limited by the disclosure.

1. A wearable heads-up display device (WHUD) comprising: a supportframe; a display carried by the support frame; a glint detection modulecarried by the support frame; at least one processor carried by thesupport frame, the at least one processor communicatively coupled to thedisplay and the glint detection module; and at least one nontransitoryprocessor-readable storage medium carried by the support frame, the atleast one nontransitory processor-readable storage mediumcommunicatively coupled to the at least one processor, wherein the atleast one nontransitory processor-readable storage medium stores data orprocessor-executable instructions that, when executed by the at leastone processor, cause the at least one processor to: cause the display todisplay at least one user interface (UI) element; and detect a gazelocation of a user based at least in part on glint information receivedfrom the glint detection module and a known location of at least one ofthe at least one UI element on the display.
 2. The WHUD of claim 1wherein the data or processor-executable instructions, when executed bythe at least one processor, cause the at least one processor to causethe display to display a number of UI elements that is below a thresholddetermined to enable accurate gaze location detection.
 3. The WHUD ofclaim 1 wherein the data or processor-executable instructions, whenexecuted by the at least one processor, cause the at least one processorto cause the display to maximize the respective distances between aplurality of UI elements displayed on the display.
 4. The WHUD of claim1 wherein the data or processor-executable instructions, when executedby the at least one processor, cause the at least one processor to causethe display to minimize a similarity between an angle and a length ofvectors that join pairs of a plurality of UI elements displayed on thedisplay.
 5. The WHUD of claim 1 wherein the data or processor-executableinstructions, when executed by the at least one processor, cause the atleast one processor to cause the display to display at least oneanimated UI element.
 6. The WHUD of claim 1 wherein the data orprocessor-executable instructions, when executed by the at least oneprocessor, cause the at least one processor to update an eye trackingcalibration based at least in part on the detected gaze location.
 7. TheWHUD of claim 6 wherein the at least one processor generates at leastone calibration point based at least in part on the detected gazelocation, the at least one calibration point comprising a glint spacepoint received from the glint detection module and a display space pointthat corresponds to a location of a UI element displayed on the display.8. The WHUD of claim 7 wherein the at least one processor generates acalibration point for each UI element displayed on the display, eachcalibration point associated with a UI element comprising a glint spacepoint received from the glint detection module and a display space pointthat corresponds to the location of the UI element on the display. 9.The WHUD of claim 1 wherein the at least one processor adds acalibration point to at least one parent calibration point model thatcomprises a plurality of calibration points to generate at least onechild calibration point model.
 10. The WHUD of claim 9 wherein the atleast one processor fits a transform to the calibration points of the atleast one child calibration point model.